Science, Faculty of
Physics and Astronomy, Department of
DSpace
UBCV
Morris, Sarah Rosemary
Doctor of Philosophy - PhD
University of British Columbia
Myelin, the lipid-rich sheath which wraps around axons, has complex and unique physical and chemical properties which can be used to produce magnetic resonance imaging (MRI) contrast. Developing MRI to measure myelin is vital for monitoring the brain and spinal cord in health and disease. This thesis explores four MRI techniques sensitive to myelin: myelin water imaging (MWI), magnetisation transfer (MT), inhomogeneous magnetisation transfer (ihMT) and diffusion imaging. First, these metrics were recorded in regions throughout the healthy adult and pediatric brain. Reproducibility, dynamic range and correlations between metrics were investigated. Healthy adult and pediatric atlases of the metrics were made publicly available for other neuroimaging researchers. Next, the orientation dependence of MWI, MT and ihMT was quantified. White matter is anisotropic, consisting of fiber bundles which vary in angle to the MRI main magnetic field. This can affect MRI metrics through the susceptibility of lipid-rich myelin, anisotropic vasculature, iron content, dipole-dipole and magic angle effects. We found that the orientation dependence curve for each metric varied between brain regions, suggesting that microstructural parameters other than fibre angle are affecting the orientation dependence. Then, we investigated the orientation dependence of ihMT in more detail, using a phospholipid bilayer sample rotated in a nuclear magnetic resonance (NMR) spectrometer to measure the variation with angle of the breadth of the lipid spectrum and dipolar order relaxation time. We found that the lipid linewidth had a greater effect on ihMT than the dipolar order relaxation time under our experimental conditions. Finally, ex vivo human spinal cord injury (SCI) tissue from the International SCI Biobank was scanned at 7 Tesla to obtain MWI, ihMT and diffusion metrics. This tissue then underwent histological staining for myelin lipids and inflammatory cells. We performed correlations between the MRI metrics and the digitised histological staining to validate the metrics’ specificity to myelin and sensitivity to inflammation processes. We measured the MRI metrics in motor and sensory white matter tracts along the cord, which provided a first glimpse into the possible utility of these techniques as biomarkers to assess the impact of SCI on myelin and axons in vivo.
Magnetic resonance imaging to measure myelin: orientationdependence and application to spinal cord injurybySarah Rosemary MorrisB. A., The University of Cambridge, 2015M. Sci., The University of Cambridge, 2016A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Physics)The University of British Columbia(Vancouver)April 2023© Sarah Rosemary Morris, 2023The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Magnetic resonance imaging to measure myelin: orientation dependenceand application to spinal cord injurysubmitted by Sarah Rosemary Morris in partial fulfillment of the requirements for thedegree of Doctor of Philosophy in Physics.Examining Committee:Cornelia Laule, Associate Professor, Physics & Astronomy, UBCSupervisorAlex MacKay, Professor, Physics & Astronomy, UBCSupervisory Committee MemberMatthew Budde, Associate Professor, Neurosurgery, MEDICAL COLLEGE OF WISCONSINExternal examinerStefan Reinsberg, Associate Professor, Physics & Astronomy, UBCUniversity examinerClaire Beasley, Associate Professor, Psychiatry, UBCUniversity examinerAdditional Supervisory Committee Members:Piotr Kozlowski, Professor, Radiology, UBCSupervisory Committee MemberBrian Kwon, Professor, Orthopaedics, UBCSupervisory Committee MemberCarl Michal, Associate Professor, Physics & Astronomy, UBCSupervisory Committee MemberWayne Moore, Clinical Professor, Pathology & Laboratory Medicine, UBCSupervisory Committee MemberiiAbstractMyelin, the lipid-rich sheath which wraps around axons, has complex and unique phys-ical and chemical properties which can be used to produce magnetic resonance imaging(MRI) contrast. Developing MRI to measure myelin is vital for monitoring the brain andspinal cord in health and disease. This thesis explores four MRI techniques sensitive tomyelin: myelin water imaging (MWI), magnetisation transfer (MT), inhomogeneousmagnetisation transfer (ihMT) and diffusion imaging.First, these metrics were recorded in regions throughout the healthy adult and pe-diatric brain. Reproducibility, dynamic range and correlations between metrics wereinvestigated. Healthy adult and pediatric atlases of the metrics were made publicly avail-able for other neuroimaging researchers.Next, the orientation dependence of MWI, MT and ihMT was quantified. Whitematter is anisotropic, consisting of fiber bundles which vary in angle to the MRI mainmagnetic field. This can affect MRI metrics through the susceptibility of lipid-richmyelin, anisotropic vasculature, iron content, dipole-dipole and magic angle effects.We found that the orientation dependence curve for each metric varied between brainregions, suggesting that microstructural parameters other than fibre angle are affectingthe orientation dependence. Then, we investigated the orientation dependence of ihMTin more detail, using a phospholipid bilayer sample rotated in a nuclear magnetic res-iiionance (NMR) spectrometer to measure the variation with angle of the breadth of thelipid spectrum and dipolar order relaxation time. We found that the lipid linewidth hada greater effect on ihMT than the dipolar order relaxation time under our experimentalconditions.Finally, ex vivo human spinal cord injury (SCI) tissue from the International SCIBiobank was scanned at 7 Tesla to obtain MWI, ihMT and diffusion metrics. Thistissue then underwent histological staining for myelin lipids and inflammatory cells. Weperformed correlations between the MRI metrics and the digitised histological stainingto validate the metrics’ specificity to myelin and sensitivity to inflammation processes.We measured the MRI metrics in motor and sensory white matter tracts along the cord,which provided a first glimpse into the possible utility of these techniques as biomarkersto assess the impact of SCI on myelin and axons in vivo.ivLay SummaryMyelin is a protective covering which increases the speed of electrical signals travellingalong nerve cells. In some diseases and after an injury, myelin is damaged and graduallylost. We used three different MRI scans to measure myelin in the brains of healthy adultsand children, assessing the reproducibility and correlation between scans. We madethis data available so other researchers can compare it with myelin measurements fromdiseased brains. In the brain, long bundles of nerve cells extend in different directions;this can affect MRI scans. We measured this effect and explored why it happens usinga model of myelin structure. Finally, we scanned spinal cords from people who diedof a spinal cord injury, to see if the scans detect myelin loss accurately and to analysewhere along the cord it occurs. This research will help to bring these new MRI scansinto clinical use for measuring myelin.vPrefaceThe work presented in this thesis was carried out under the supervision of Dr CorneliaLaule. I also received valuable guidance throughout my PhD research from my commit-tee members Drs Piotr Kozlowski, Brian Kwon, Alex MacKay, Carl Michal and WayneMoore. All of the MRI data presented in this thesis was acquired at the UBC MRIResearch Centre, on the human 3 Tesla or small-animal 7 Tesla scanner. The NMRdata was acquired in the UBC NMR Physics laboratory. All MRI scanning acquiredat the UBC MRI Research Centre was approved by the UBC Clinical Research EthicsBoard: the normal volunteer scanning in Chapter 3 and Chapter 4 (certificate number:H03-70327), the pediatric study in Chapter 3 (certificate number: H08-01539) and theInternational Spinal Cord Injury Biobank (certificate number: H19-00690).Chapter 3 Section 3.1 was published: Morris, SR, Vavasour, IM, Smolina, A,MacMillan, EL, Gilbert, G, Lam, M, Kozlowski, P, Michal, CA, Manning, A, MacKay,AL, Laule, C. Myelin biomarkers in the healthy adult brain: Correlation, repro-ducibility, and the effect of fiber orientation. Magnetic Resonance in Medicine. 89,1809-1824 (2023). This work was also presented in an oral presentation Inhomoge-neous magnetization transfer in the healthy adult brain: reproducibility and correla-tion with MTR and myelin water imaging at the 29th Annual Meeting of the ISMRM,virtual, 16-20th May 2021 and a poster presentation Measuring the Effect of WhiteviMatter Fibre Direction on MRI Measurements of Myelin at the ACTRIMS Forum,San Diego, USA, 23-25th Feb 2023. The study design and most of the data collectionwas performed by Drs Vavasour, MacKay and Laule. I collected data for the last 6participants. Initial analysis of the data, and the MWI analysis was performed by MsSmolina and Dr Vavasour. I made the ihMTR and MTR maps, processed the DWI data,performed the registrations, chose the regions of interest, and conducted the correlation,reproducibility and fibre orientation dependence analyses. The idea to plot the orienta-tion dependence of each region of interest separately came up in a discussion betweenDrs Laule, MacKay and myself. I also interpreted the results and wrote the manuscript.Drs Mackay and Laule provided instructive feedback on my interpretations of the data.All other coauthors provided feedback on the manuscript content.Chapter 3 Section 3.2 is published as Morris SR, Holmes RD, Dvorak AV, Liu H,Yoo Y, Vavasour IM, Mazabel S, Ma¨dler B, Li D, Siegel L, Beaulieu C, MacKay A,Laule C. Brain Myelin Water Fraction and Diffusion Tensor Imaging Atlases for 9-10Year-Old Children. Journal of Neuroimaging 30(2), 150-60 (2020). [1] The pediatricatlas was also presented in a powerpitch A pediatric brain template for myelin wa-ter fraction and diffusion tensor imaging at the 27th Annual Meeting of the ISMRM,Montreal, QC, Canada, May 11-16, 2019 and as a poster A Pediatric Atlas for MultipleSclerosis Myelin Water and Diffusion Tensor Imaging Studies at the ACTRIMS Fo-rum and NAIMS Cooperative, Dallas, TX, USA, Feb 18-March 2 2019. Drs Beaulieu,MacKay and Laule designed the study and collected the data. Dr Liu provided helpfuladvice on the FSL processing. Mr Dvorak created the atlases using ANTs. I performedall of the region of interest registration and statistical analyses, comparing qMRI met-rics and also comparing pediatric and adult values. I interpreted the results and wrotethe manuscript. All other coauthors provided feedback on the manuscript content.The data in Chapter 4 Section 4.1 is also presented in the aforementioned paperviiMyelin biomarkers in the healthy adult brain: correlation, reproducibility, and theeffect of fibre orientation. which is in press at Magnetic Resonance in Medicine.Chapter 4 Section 4.2 is published as Morris SR, Frederick R, MacKay AL, LauleC, Michal CA. Orientation dependence of inhomogeneous magnetization transfer anddipolar order relaxation rate in phospholipid bilayers. Journal of Magnetic Reso-nance 338, 107205 (2022). [2] It was also presented in an oral presentation Orientationdependence of inhomogeneous magnetization transfer and dipolar order relaxationtime in a phospholipid bilayer sample at the 28th Annual Meeting of the ISMRM,virtual, August 8-14, 2020. Dr Michal conceived of the study design. Ms Frederick de-veloped the pulse sequences and collected a first round of data to investigate the sourceof ihMT orientation dependence (that data is not used in this thesis). I assembled thebilayer sample and collected and analysed all of the NMR data under the supervisionof Drs Michal and MacKay who provided constructive advice throughout. I wrotethe manuscript. Drs Laule, Michal and MacKay all gave instructive feedback on themanuscript content.Chapter 5 Section 5.2 is in the final stages of preparation as a paper Morris SR,Swift-Lapointe T, Yung A, Prevost V, George SI, Kozlowski P, Bauman A, SamadiF, Fournier C, Mattu P, Parker L, Velenosi A, Streijger F, Moore GRW, Hirsch-Reinshagen V, Kwon BK and Laule C. Novel MRI biomarkers of the injured spinalcord – a comparative study of imaging and histology in human traumatic SCI. whichwe plan to submit in the next few months. This data has also been presented at manyinternational conferences:1. Diffusion MRI Biomarkers for Inflammation in Spinal Cord. North AmericanImaging in MS Cooperative Workshop, oral presentation, San Diego, CA, USA,21-22nd Feb 2023.viii2. Diffusion MRI Biomarkers for Inflammation in Spinal Cord. Americas Com-mittee for Treatment and Research in Multiple Sclerosis, poster presentation, SanDiego, CA, USA, 21-22nd Feb 2023.3. Identifying diffusion MRI biomarkers for inflammation in human traumaticspinal cord injury. 61st International Spinal Cord Society Annual ScientificMeeting, oral presentation, Vancouver, BC, Canada, 15-18th Sept 2022.4. Myelin-sensitive MRI metrics correlate with inflammation in human traumaticspinal cord injury. 31th Annual Meeting of the ISMRM, poster presentation,London, UK, 7-12 May 2022.5. Identifying diffusion model biomarkers for inflammation in human traumaticspinal cord injury., 31th Annual Meeting of the ISMRM, oral presentation, Lon-don, UK, 7-12 May 2022.6. Evaluating advanced MRI biomarkers for axon and myelin loss after humantraumatic spinal cord injury. Annual Scientific Meeting of the InternationalSpinal Cord Injury Society, oral presentation, virtual, 29 Sept-2 Oct 2021.7. Correlating advanced MRI and histopathological measurements of axons andmyelin in human traumatic spinal cord injury. 29th Annual Meeting of theISMRM, oral presentation, virtual, 16-20 May 2021.8. Investigating and validating quantitative MRI biomarkers for demyelination,axon integrity and inflammation after traumatic spinal cord injury. 21th AnnualScientific Conference of the Canadian Spine Society, oral presentation, virtual,Feb 3-24 2021.9. Measuring Demyelination, Axonal Loss And Inflammation After Acute AndixSubacute Injury To Human Spinal Cord With Quantitative Magnetic Reso-nance Imaging And Histopathology. ACTRIMS Forum and NAIMS Coopera-tive, poster presentation, West Palm Beach, FL, USA, February 25-27, 2020.10. Advanced Magnetic Resonance techniques in Post-Mortem Human Spinal CordInjury: Correlations with Histopathology. Gordon Research Conference on Tis-sue Microstructure Imaging, poster presentation, South Hadley, MA, USA, July7-12, 2019.11. Advanced Magnetic Resonance techniques in Post-Mortem Human Spinal CordInjury: Correlations with Histopathology. 27th Annual Meeting of the ISMRM,oral presentation, Montreal, QC, Canada, May 11-16, 2019.Chapter 5 Section 5.3 was presented in an oral presentation Inhomogeneous Magne-tization Transfer and DBSI detect downstream white matter damage in post-mortemhuman cervical spinal cord injury at the 28th Annual Meeting of the ISMRM, virtual,August 8-14, 2020.This was a very collaborative project. Dr Kwon founded the biobank and providedsupervision, organisation and guidance for the whole project. Mr Velenosi manages thebiobank physical and digital repositories and data sharing. Ms Parker and Mr Mattuperformed spinal cord extractions in the morgue. Mr Yung supervised the collectionof 7 Tesla advanced MRI at the UBC MRI Research Centre and designed a pipelinewhich performs all of the preprocessing and produces qMRI maps. Dr Kozlowski andMr Bauman also assisted with this step. Dr Prevost designed the new ihMT sequencewe used to collect this data. Drs Moore, Hirsch-Reinshagen, Laule and myself sectionedthe cords into 3 mm slices in the morgue for paraffin embedding. Ms Samadi and MsFournier performed the histological preparation and staining at ICORD. Ms Swift La-Pointe and Ms George both undertook summer research projects under my supervision,xperforming some of the manual segmentations and statistical analyses. I planned thedata analysis and, with help from Mr Yung, researched and designed the registration andsegmentation methods. I performed the statistical analysis, made the graphs, interpretedthe results and wrote the paper and abstracts. All coauthors provided valuable feedbackon the manuscript and conference abstract contents.xiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviiiAcronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxivSymbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xxviiiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxxii1 Magnetic resonance imaging . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 MRI basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Excitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Relaxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5xii1.3.1 Factors affecting T1 and T2 . . . . . . . . . . . . . . . . . . . . 71.4 Spin echo sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.5 Gradient echo sequences . . . . . . . . . . . . . . . . . . . . . . . . . 111.6 Spatial localisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.7 Clinical MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.8 Other rapid imaging sequences used in this thesis . . . . . . . . . . . . 161.8.1 EPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.8.2 RARE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.8.3 FLASH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.8.4 IR-TFE/MPRAGE . . . . . . . . . . . . . . . . . . . . . . . . 171.9 Quantitative MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.10 Myelin water imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.10.1 Acquisition acceleration . . . . . . . . . . . . . . . . . . . . . 191.10.2 MWI analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 211.11 Magnetisation transfer imaging . . . . . . . . . . . . . . . . . . . . . . 231.12 Inhomogeneous magnetisation transfer imaging . . . . . . . . . . . . . 241.12.1 Dipolar coupling . . . . . . . . . . . . . . . . . . . . . . . . . 261.12.2 Provotorov theory . . . . . . . . . . . . . . . . . . . . . . . . 281.12.3 T1D filtering and “boost” effect . . . . . . . . . . . . . . . . . . 291.12.4 Reducing sensitivity to T1 and B1 inhomogeneities . . . . . . . 301.13 Diffusion imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311.13.1 Diffusion Tensor Imaging . . . . . . . . . . . . . . . . . . . . 341.13.2 Diffusion Kurtosis Imaging . . . . . . . . . . . . . . . . . . . 371.13.3 Diffusion Basis Spectrum Imaging . . . . . . . . . . . . . . . . 371.14 Validating quantitative MRI techniques . . . . . . . . . . . . . . . . . 41xiii2 Myelin in the central nervous system . . . . . . . . . . . . . . . . . . . . . 432.1 Central nervous system . . . . . . . . . . . . . . . . . . . . . . . . . . 432.1.1 Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452.1.2 Spinal cord . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482.2 Myelin structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502.3 Myelin development . . . . . . . . . . . . . . . . . . . . . . . . . . . 502.3.1 qMRI to measure myelin development . . . . . . . . . . . . . . 502.4 Demyelination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512.5 Spinal cord injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 522.5.1 Wallerian degeneration . . . . . . . . . . . . . . . . . . . . . . 532.5.2 ISNCSCI Scale . . . . . . . . . . . . . . . . . . . . . . . . . . 542.5.3 qMRI for spinal cord injury . . . . . . . . . . . . . . . . . . . 552.6 Thesis overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 Myelin imaging in the healthy brain . . . . . . . . . . . . . . . . . . . . . 583.1 Myelin biomarkers in the healthy adult brain . . . . . . . . . . . . . . . 603.1.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643.1.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783.2 Myelin biomarkers in the healthy pediatric brain . . . . . . . . . . . . . 793.2.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 953.3 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96xiv4 The orientation dependence of myelin imaging metrics in white matter . 974.1 Orientation dependence in the healthy adult brain . . . . . . . . . . . . 984.1.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994.1.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1034.1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1074.2 Causes of orientation dependence in ihMT . . . . . . . . . . . . . . . . 1084.2.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1084.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1214.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1254.3 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1275 Applying myelin imaging to human traumatic spinal cord injury . . . . . 1295.1 The International Spinal Cord Injury Biobank . . . . . . . . . . . . . . 1305.2 Measuring MRI metric sensitivity to myelin and inflammation . . . . . 1365.2.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1375.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1395.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1455.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1555.3 Tracking MRI metric changes along the cord . . . . . . . . . . . . . . 1565.3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1565.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1575.3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1625.3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1625.4 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163xv6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168xviList of TablesTable 3.1 List of the region of interest abbreviations used. . . . . . . . . . . . 66Table 3.2 The inter-subject and scan-rescan region of interest data for MTR,ihMTR and MWF. . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Table 3.3 The correlation statistics for the Pearson and Spearman correlationsbetween MTR, ihMTR and MWF. . . . . . . . . . . . . . . . . . . 69Table 3.4 Region of interest values from pediatric and adult brain atlases. . . . 85xviiList of FiguresFigure 1.1 M⃗ is tipped away from thermal equilibrium using a magnetic fieldB⃗1 oscillating at ω1, viewed in both the lab and rotating frames. . . 4Figure 1.2 Faraday induction in a receiver coil due to precession of M⃗. . . . . . 5Figure 1.3 The exponential decay of the transverse magnetisation (Mxy) and re-growth of the longitudinal magnetisation (Mz) in the rotating frame,defining the relaxation time constants T1 and T2. . . . . . . . . . . . 7Figure 1.4 The effect of molecular “tumbling” rate on T1 and T2 relaxationtimes and characteristic T1 and T2 values for sources of 1H in tis-sue. Adapted from [3] by Allen D. Elster. . . . . . . . . . . . . . . 8Figure 1.5 A spin echo pulse sequence, showing the T′2 decay being refocussedby the 180◦ RF pulse. . . . . . . . . . . . . . . . . . . . . . . . . . 10Figure 1.6 A gradient echo pulse sequence, showing dephasing and rephasingof the transverse magnetisation (Mxy) by gradients. . . . . . . . . . 12Figure 1.7 Commonly used contrasts in MRI: T1-, T2- and PD-weighted scans,and their required TE and TR times. Figure adapted from one cour-tesy of Allen D. Elster. [3] . . . . . . . . . . . . . . . . . . . . . . 16Figure 1.8 Multi-echo spin echo pulse sequence, used to measure the T2 decaycurve for MWI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19xviiiFigure 1.9 GRASE pulse sequence, used to measure the T2 decay curve for MWI. 20Figure 1.10 The T2 distribution fit using NNLS and how it is used to calculatethe MWF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Figure 1.11 The off-resonance pre-pulse applied to the broad non-aqueous spec-trum and subsequent magnetisation transfer to the aqueous 1H. . . . 24Figure 1.12 Dipolar coupling between two spins. . . . . . . . . . . . . . . . . . 26Figure 1.13 Two different dual off-resonance pre-pulse application methods, re-sulting in signal acquisition with and without T1D-filtering. . . . . . 30Figure 1.14 Diffusion-weighted MRI pulse sequence. . . . . . . . . . . . . . . 32Figure 1.15 The diffusion propagator for Gaussian diffusion compared to diffu-sion in biological tissues, where the kurtosis K > 0. . . . . . . . . . 38Figure 1.16 The difference between DTI and DBSI fitting of diffusion data. Re-produced with permission from Cross and Song [4]. . . . . . . . . . 40Figure 2.1 The structure of a neuron, from OpenStax Anatomy and Physiology[5], under Creative Commons Attribution 4.0 International (CC BY4.0). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Figure 2.2 The environment of white matter, including oligodendrocytes, astro-cytes and microglia, from OpenStax Anatomy and Physiology [5],under Creative Commons Attribution 4.0 International (CC BY 4.0). 45Figure 2.3 T1-weighted MRI of the brain, showing the white matter and greymatter anatomy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Figure 2.4 White matter and grey matter brain regions of interest used in thisthesis, from the JHU White Matter Labels and MNI structural atlasrespectively. [6–8] . . . . . . . . . . . . . . . . . . . . . . . . . . 47Figure 2.5 Segments of the human spinal cord. . . . . . . . . . . . . . . . . . 48xixFigure 2.6 T2-weighted MRI of the spinal cord cross-section, showing the whitematter and grey matter “butterfly” shape. . . . . . . . . . . . . . . 49Figure 2.7 Spinal cord regions of interest used in this thesis, from the histolog-ical atlas made by Sengul et al. [9]. . . . . . . . . . . . . . . . . . 49Figure 2.8 The structure and composition of myelin. Reproduced with permis-sion from Laule et al. [10]. . . . . . . . . . . . . . . . . . . . . . . 51Figure 2.9 The process of myelin loss after traumatic SCI. Reproduced withpermission from Textbook of Neural Repair and Rehabilitation [11]. 53Figure 2.10 The processes of retrograde and Wallerian degeneration after trau-matic SCI. Reproduced with permission from Textbook of NeuralRepair and Rehabilitation [11]. . . . . . . . . . . . . . . . . . . . . 54Figure 3.1 Example MTR, ihMTR and MWF maps from five participants. . . . 65Figure 3.2 The rankings of white and grey matter regions by MTR, ihMTR andMWF. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68Figure 3.3 Pearson correlations between MTR, ihMTR and MWF in the healthyadult brain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70Figure 3.4 Pearson correlations between MTR, ihMTR and MWF in the healthyadult brain separated by region. . . . . . . . . . . . . . . . . . . . 72Figure 3.5 Bland-Altman graphs analysing the reproducibility of MTR,ihMTRand MWF in healthy adults . . . . . . . . . . . . . . . . . . . . . . 74Figure 3.6 The pediatric atlas creation process. . . . . . . . . . . . . . . . . . 82Figure 3.7 The regions chosen for the pediatric MWF brain atlas. . . . . . . . 83Figure 3.8 Five representative axial slices of the MWF, FA, MD, AD and RDpediatric atlas metric maps. . . . . . . . . . . . . . . . . . . . . . . 86xxFigure 3.9 Single slices displaying MWF, FA, MD, AD and RD mean and stan-dard deviation from the pediatric atlas. . . . . . . . . . . . . . . . . 87Figure 3.10 Comparison and correlation between adult and pediatric MWF. . . 88Figure 3.11 Correlations between MWF and DTI metrics in the pediatric brain. . 90Figure 4.1 Apparent orientation dependence of MTR, ihMTR and MWF, sep-arated by participant and region. . . . . . . . . . . . . . . . . . . . 100Figure 4.2 The apparent orientation dependence of ihMTR, MTR, and MWFfor each region shown separately. . . . . . . . . . . . . . . . . . . 101Figure 4.3 Histograms showing the number of voxels at each fibre angle foreach region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102Figure 4.4 Investigating the trends of ihMTR and MWF with fibre angle andapproximate axon size in the corpus callosum and the cerebral pe-duncles/internal capsules. . . . . . . . . . . . . . . . . . . . . . . . 103Figure 4.5 Definition of the bilayer orientation angle α , with respect to externalmagnetic field B0 and the myelin bilayer phantom in the NMR tube. 108Figure 4.6 Pulse sequences for the NMR POPC bilayer experiments. . . . . . . 110Figure 4.7 The geometry used to define the fiber model. . . . . . . . . . . . . 113Figure 4.8 Representative FIDs from the aligned POPC bilayer sample at dif-ferent angles to B0. . . . . . . . . . . . . . . . . . . . . . . . . . . 114Figure 4.9 Orientation dependence of M2, R1D and ihMTR in aligned POPClipids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116Figure 4.10 Relaxation of dipolar order in aligned POPC. . . . . . . . . . . . . 117Figure 4.11 Orientation dependence of R1D and ihMTR for the fiber model sim-ulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119xxiFigure 4.12 Fiber model simulation including a Gaussian distribution of fiberdirections with widths of 10◦ and 20◦ around the main fiber direction. 120Figure 5.1 Demographics and injury details for the eight patients from the IS-CIB included in the studies in Chapter 5 of this thesis. . . . . . . . 131Figure 5.2 The biobank tissue processing after 7 T MRI. . . . . . . . . . . . . 133Figure 5.3 High resolution images of Luxol Fast Blue (LFB) staining for myelinphospholipids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Figure 5.4 High resolution images of Class II Major Histocompatibility Com-plex (Class II MHC) and Cluster of Differentiation 68 (CD68) stain-ing for inflammatory cells. . . . . . . . . . . . . . . . . . . . . . . 136Figure 5.5 An example manual region of interest segmentation of the histologydata, carefully excluding tissue rips and blood vessels. . . . . . . . 138Figure 5.6 MRI metric maps from the six patients included in this study. . . . . 140Figure 5.7 Representative histological staining images from the six patients in-cluded in this study. . . . . . . . . . . . . . . . . . . . . . . . . . . 142Figure 5.8 Heatmaps showing the Pearson correlation strength (r) across everyvariable in the dataset. . . . . . . . . . . . . . . . . . . . . . . . . 143Figure 5.9 All fifteen MRI metrics correlated with LFB stain area fraction. . . 144Figure 5.10 DTI/DKI metrics correlated with Class II MHC and CD68 stain areafraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146Figure 5.11 Myelin-sensitive MRI metrics correlated with Class II MHC andCD68 stain area fraction. . . . . . . . . . . . . . . . . . . . . . . . 147Figure 5.12 Correlations between the histology stain fractions: Class II MHC,CD68, and LFB. . . . . . . . . . . . . . . . . . . . . . . . . . . . 148xxiiFigure 5.13 7 Tesla high-resolution post-mortem anatomical scans for each pa-tient included in this study. Three slices are shown: one at injuryepicentre, one 4.5 cm above and one 4.5 cm below. . . . . . . . . . 156Figure 5.14 DTI metrics for each patient in each white matter tract, along 13.5cm of the cord, centered on the injury epicentre. . . . . . . . . . . . 159Figure 5.15 DBSI metrics for each patient in each white matter tract, along 13.5cm of the cord, centered on the injury epicentre. . . . . . . . . . . . 160Figure 5.16 ihMTRinv for each patient in each white matter tract, along 13.5 cmof the cord, centered on the injury epicentre. . . . . . . . . . . . . . 161Figure 5.17 Representative slices of the axial ihMTRinv maps from above andbelow the epicentre in Cases 7 and 8, displaying visible Walleriandegeneration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161xxiiiAcronyms3DT1 3D T1-weighted scan.AD Axial Diffusivity.ADRF/ARRF Adiabatic Demagnetization in the Rotating Frame/Adiabatic Remagne-tization in the Rotating Frame.AIS ASIA Impairment Scale.ALS Amyotrophic Lateral Sclerosis.ANTs Advanced Normalisation Tools.CD68 Cluster of Differentiation 68.Class II MHC Class II Major Histocompatibility Complex.CoV Coefficient of Variation.CSF Cerebrospinal Fluid.CT Computed Tomography.DBSI Diffusion Basis Spectrum Imaging.xxivDKI Diffusion Kurtosis Imaging.DTI Diffusion Tensor Imaging.DWI Diffusion-Weighted Imaging.EPI Echo Planar Imaging.FA Fractional Anisotropy.FID Free Induction Decay.FIX Fixation Interval.FLASH Fast Low-Angle SHot.FoV Field of View.GE Gradient Echo.GRASE GRadient And Spin Echo.H&E Hematoxylin and Eosin stain.I/EW gmT2 Intra-/Extracellular Water geometric mean T2.IDI Injury-Death Interval.ihMT inhomogeneous Magnetisation Transfer.ihMTR inhomogeneous Magnetisation Transfer Ratio.ihMTRf ihMTR with T1D-filtering.ihMTRinv inverse inhomogeneous Magnetisation Transfer Ratio.xxvihMTRuf ihMTR with no T1D-filtering.IR Inversion recovery.ISCIB International Spinal Cord Injury Biobank.ISNCSCI International Standards for Neurological Classification of Spinal Cord Injury.LFB Luxol Fast Blue.mcDESPOT multicomponent Driven Equilibrium Single Pulse Observation of T1 andT2.MD Mean Diffusivity.MK Mean Kurtosis.MPRAGE Magnetization Prepared - RApid Gradient Echo.MRI Magnetic Resonance Imaging.MS Multiple Sclerosis.MT Magnetisation Transfer.MTR Magnetisation Transfer Ratio.MWF Myelin Water Fraction.MWI Myelin Water Imaging.NMR Nuclear Magnetic Resonance.NNLS Non-Negative Least Squares.xxviNRMSE Normalised Root Mean Squared Error.PD Proton Density.plp-GFP proteolipid protein Green Fluorescent Protein.PMI Post-Mortem Interval.POPC 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine.qMRI quantitative Magnetic Resonance Imaging.RARE Rapid Acquisition with Refocused Echoes.RD Radial Diffusivity.RF Radiofrequency.RK Radial Kurtosis.ROI Region Of Interest.SAR Specific Absorption Rate.SCI Spinal Cord Injury.SE Spin Echo.SNR Signal-to-Noise Ratio.TE Echo Time.TFE Turbo Field Echo.TR Repetition Time.UBC University of British Columbia.xxviiSymbolsα1 Flip angle achieved by the excitation RF pulse B1.γ Gyromagnetic ratio.µ0 Vacuum permeability.χ0 Magnetic susceptability.ω0 Larmor frequency in the main magnetic field B0.ω1 Frequency of oscillation of the transmitted field, B1, used to excite the spins out ofequilibrium.ωD Average dipolar coupling strength.ℏ Reduced Planck constant.b Diffusion sequence weighting, varied using the diffusion gradients’ strength, timingand duration.B0 Magnitude of the main static magnetic field in MRI.B⃗0 Main static magnetic field in MRI.B1 Magnitude of the transmitted magnetic field (from the RF pulse).xxviiiB⃗1 Transmitted magnetic field (from the RF pulse).B1rms Root mean squared average of the transmitted field over the time it is applied.D Fick diffusion coefficient in Einstein’s equation.1H Hydrogen nucleus (which is a single proton).ĤD Dipolar Hamiltonian.ĤRF RF Hamiltonian.ĤZ Zeeman Hamiltonian.K Diffusion kurtosis parameter.k Boltzmann constant.M⃗ Bulk magnetisation vector.m⃗ Magnetic moment of a nucleus.M0 Magnitude of the bulk magnetisation at equilibrium.M⃗0 Bulk magnetisation vector at equilibrium.M2 Second moment of a spectrum.Mxy Magnitude of the bulk magnetisation vector in the transverse direction (perpendic-ular to B⃗0).Mz Magnitude of the bulk magnetisation vector in the longitudinal direction (along B⃗0).R1D Dipolar order relaxation rate, rate of decay of local dipolar order in a spin system.xxixR2 Transverse relaxation rate, the characteristic rate for the xy component of the bulkmagnetisation M, to decay to zero, = 1/T2.S0 Image obtained with no off-resonance pre-pulse.S+ Image obtained with an off-resonance pre-pulse at a frequency higher than the reso-nance frequency.S− Image obtained with an off-resonance pre-pulse at a frequency lower than the reso-nance frequency.S± Image obtained with a pre-pulse at dual off-resonance frequency, with the first pulsebeing at a frequency higher than the resonance frequency.S∓ Image obtained with a pre-pulse at dual off-resonance frequency, with the first pulsebeing at a frequency lower than the resonance frequency.T1 Longitudinal relaxation time, the characteristic time for the z component of the bulkmagnetisation, M, to return to its equilibrium value along the direction of B0.T1D Dipolar order relaxation time, the characteristic time for the decay of local dipolarorder in a spin system.T2 Transverse relaxation time, the characteristic time for the xy component of the bulkmagnetisation, M, to decay to zero.T′2 Transverse relaxation due to external mechanisms such as B0 inhomogeneity andsusceptibility-induced field distortions.T ∗2 Observed or effective transverse relaxation which is a combination of intrinsic,atomic and molecular mechanisms and external mechanisms such as B0 inhomo-geneity and susceptibility-induced field distortions.xxxT I Inversion time, the time after a 180◦ pulse in which the longitudinal relaxation isallowed to recover, before a 90◦ pulse is applied to measure the signal, used todetermine T1.xxxiAcknowledgmentsI am so grateful to all of my friends and colleagues in Vancouver who have made my timeat UBC so much fun. I particularly want to thank my supervisor, Dr Corree Laule, forher kindness and unfailing support, for cutting hundreds of unnecessary words in late-night ISMRM abstract edits, and for those delicious pretzels. My committee members,Drs Piotr Kozlowski, Brian Kwon, Alex MacKay, Carl Michal, and Wayne Moore, havealso been wonderful mentors and I am so grateful for their wisdom and advice. The UBCMRI group is such a great place to work: I want to thank Irene, Erin, Adam, Lisa, Carina,Kevin, Shannon, Christian, Jon, Tigris, Alex E., Andrew Y., Kirsten, Laura, Sharada,Poljanka, Neale, and everyone else who made going conferences and the weekly MRIphysics meetings such a pleasure. I am also very grateful for my NSERC CGS-D schol-arship, which gave me the freedom to focus on my research.Three of the five and a half years of my PhD were during and after the COVID-19pandemic. I am so grateful to my friends and family for making what could have been alonely time, less isolated. Mum, Dad and Katharine thank you for being so supportive,always having time for an 8-hour-time-difference evening chat and not minding thatthe birthday cards I send always get to England very late. Catriona, I’m so glad youlive on the same continent as me now; our sporadic zoom calls are always excellent.Kate, Alex and Daisy you are the best roommates. Thank you for all of the deliciousxxxiifood, homemade kombucha, late-night TV, and beach days. Particular thanks to Daisyfor forcing me to go for a walk every morning while writing this thesis. Also Bretta,Hannah, Sarah W, Branch, Sam, Harry, Dabney, Ryan, Erik, Emilie and Cassandra,you’re all wonderful and hanging out with you is the highlight of my week. Finally,Luke - thank you for the hikes, the music, and for successfully cutting to size that glassNMR tube, the last one left in lab, when I had broken the second-to-last one and had tore-take all of the ihMTR data at 9pm. Oh, and for proofreading this.xxxiiiChapter 1Magnetic resonance imagingMagnetic Resonance Imaging (MRI) was first developed by Paul Lauterbur and PeterMansfield in the 1970s. [12] They, along with many others, refined MRI acquisitionand reconstruction, leading to MRI scanners being installed for clinical use in the early1980s. Since then both scanner and computer technology has greatly improved and MRIis a vital part of modern healthcare. Lauterbur and Mansfield won the Nobel Prize inMedicine for the development of MRI in 2003.1.1 MRI basicsDuring an MRI scan, the human body is positioned in a very strong magnetic field, B⃗0,produced by a superconducting electromagnet.1 Nuclei with an odd number of pro-tons and/or neutrons, have a non-zero total spin angular momentum Sˆ and therefore a1The material from this chapter comes from my own general knowledge, the very informative MRIcourse (PHYS542) taught by Qing-San Xiang and Alex MacKay when I took it in 2018, Spin Dynamicsby Malcolm H. Levitt [13] and my favourite textbook, Questions and Answers in Magnetic ResonanceImaging by Allan D. Elster [3].1magnetic moment2,m⃗ = γ Sˆ (1.1)This proportionality can be derived using the Wigner-Eckhart theorem. The propor-tionality constant, γ , is called the gyromagnetic ratio and is specific to the particle inquestion. 1H, a single proton,3 is the most abundant nucleus in the human body and sois the focus of most MRI, although 13C, 19F, 31P and 23Na can also be imaged.1H nuclei, interact with B⃗0 via the Hamiltonian:ĤZ =−m⃗ · B⃗0 (1.2)and experience Zeeman splitting into two energy states with an energy difference:∆E = γℏB0 (1.3)where ℏ is the reduced Planck constant.At thermal equilibrium, 1H will distribute between these two states according to theBoltzmann distribution,N−N+= exp(γℏB0kT)(1.4)where k is the Boltzmann constant, T is the temperature of the system, and N± are thenumber of nuclei one would expect to measure in the higher and lower energy statesrespectively. This results in a slightly greater number of 1H aligned with B⃗0 (the lowerenergy state) than anti-aligned. In fact, for B0 = 3 Tesla at body temperature, there areonly around 10 excess aligned 1H for every 1 million nuclei. However, over the verylarge number of 1H nuclei in the body, this asymmetry produces a significant net mag-2In this thesis, vectors are denoted with an arrow (⃗x) and quantum mechanical operators and unit vectorswith a hat (xˆ). Anything else is a scalar quantity or constant.3In MRI physics, 1H nuclei are often referred to as “protons” or “spins”.2netisation at equilibrium, M⃗0 which is proportional and parallel to the external magneticfield B⃗0.M⃗0 = χ0B⃗0 (1.5)where χ0 is the magnetic susceptability of the material.To obtain the expectation value of a single magnetic moment, m⃗, in the B⃗0 field, wecan use the Ehrenfest theorem which reduces to:ddt⟨m⃗⟩= iℏ⟨[ĤZ, m⃗]⟩=iℏ⟨[−m⃗ · B⃗0, m⃗]⟩= ⟨m⃗× B⃗0⟩(1.6)using angular momentum operator commutation properties. This corresponds to a pre-cession of M⃗ about B⃗0 at the Larmor frequency:ω0 = γB0 (1.7)Here we have considered a single magnetic moment, but the same can be found for themacroscopic magnetisation, M⃗.1.2 ExcitationTo tip M⃗ into the transverse plane, we use a magnetic field B⃗1. B⃗1 is oscillating at afrequency ω1 which is near the 1H resonance frequency, ω0, and applied in a directionperpendicular to B⃗0. A useful analogy is to think of pushing a swing; the pushes will bemost effective if they are in phase with the swinging. For 1H, ω0 is usually in the MHzto GHz range, so B1 is called the Radiofrequency (RF) or RF pulse.In the frame rotating at ω0, B⃗0 has no effect on the spins and if ω1 is actually equal3Figure 1.1: M⃗ is tipped away from thermal equilibrium using a magnetic field B⃗1oscillating at ω1, viewed in both the lab and rotating frames.to ω0, B⃗1 is a static field. M⃗ begins to precess around B⃗1 achieving a flip angle awayfrom B⃗0 of α1. (In the lab frame, M⃗ precesses due to both B⃗0 and B⃗1 resulting in aspiralling motion called nutation.) For a strong rectangular RF pulse at ω0, with constantamplitude B1 and duration, t:α1 = γB1t (1.8)The longer this field is applied, the further M⃗ is tipped away from equilibrium. Note thatB1 is generally many orders of magnitude weaker than B0 and so precession at ω1 willbe much slower than precession at ω0.Once tipped away from its equilibrium parallel to B⃗0, the precession of the net mag-netisation M⃗ can be measured by Faraday induction in a receiver coil (Figure 1.2). Theinduced voltage is proportional to the changing magnetic flux through the coil.V =dΦdt= 4πNAMx(1.9)where c is the speed of light and Φ is the total magnetic flux through the coil, which has4Figure 1.2: Faraday induction in a receiver coil due to precession of M⃗.number of turns, N and area A. If Mx = M0 sinω0t, which is true assuming spins are inphase and spin dephasing is occurring slowly compared to the precession frequency:V =4πNAω0M0ccosω0t (1.10)Both M0 and ω0 depend linearly on B0, which means V depends quadratically on B0 andso using higher field strengths can greatly increase the Signal-to-Noise Ratio (SNR) inMRI.1.3 RelaxationAfter M⃗ is tipped away from B⃗0 by an RF pulse, Equation 1.6 would suggest that it willprecess around B0 indefinitely. However, in reality the individual spins which form M⃗5will interact with each other and the environment to return M⃗ to thermal equilibrium.Bloch defined two phenomenological relaxation time constants to describe this process,T1 and T2, and introduced the Bloch equation [14]:dM⃗dt= γM⃗× B⃗− Mxxˆ+MyyˆT2− (Mz−M0)zˆT1(1.11)where we have used the convention that B⃗0 is in the z direction, and M⃗0 lies along +z.T1 relaxation is stimulated by local magnetic fields which bring Mz back to its equi-librium value, M0, via energy loss to the environment. T2 relaxation occurs in the xyplane from loss of phase coherence due to variations in local field strength and spin-spininteractions.The Bloch equation can be solved, assuming that at t = 0, Mx = 0, My = My(0),Mz = Mz(0):Mx(t) = My(0)sinω0t exp(− 1T2)My(t) = My(0)cosω0t exp(− 1T2)Mz(t) = M0+(Mz(0)−M0)exp(− 1T1) (1.12)The transverse magnetisation, precessing around B⃗0 at ω0, decaying over time and mea-sured via Faraday induction, is called the Free Induction Decay (FID).The transverse Bloch equation can be simplified by moving to a frame of referencerotating at the Larmor frequency. Within this reference frame, the longitudinal magneti-sation (Mz) equation remains the same, but the transverse magnetisation (Mxy) becomes:MT (t) = MT (0)exp(− 1T2)(1.13)6Figure 1.3: The exponential decay of the transverse magnetisation (Mxy) and re-growth of the longitudinal magnetisation (Mz) in the rotating frame, definingthe relaxation time constants T1 and T2.1.3.1 Factors affecting T1 and T2The theory behind relaxation in pure water, simple solutions, and homogeneous solids ismostly well understood, however relaxation in tissue is significantly more complicated,as it contains many different aqueous and non-aqueous compartments of 1H which inter-act and exchange.4 Aqueous 1H are those on water molecules. Non-aqueous 1H protonsare those on lipids and macromolecules. There is a third compartment of 1H which areon water molecules tightly or loosely bound to macromolecules, which have differentrelaxation properties to the “free water”, called the “hydration water” (see Figure 1.4 forrelaxation times). No comprehensive theory has been described to date which explainsevery mechanism contributing to T1 and T2 relaxation in tissues. Nonetheless, someof the principal mechanisms and how they might be affected by tissue microstructuredifferences have been described.T1 relaxation involves transferal of energy from the nuclear spins to the environment.Einstein showed that the probability of spontaneous emission of energy is proportional4Many terms are used to differentiate free protons, also called water protons, aqueous protons or un-bound protons, from bound protons which are also called non-aqueous protons, macromolecular protonsor semi-solid protons...7Figure 1.4: The effect of molecular “tumbling” rate on T1 and T2 relaxation timesand characteristic T1 and T2 values for sources of 1H in tissue. Adapted from[3] by Allen D. Elster.to the frequency of the emitted photon cubed, ω3. In the RF frequency range, this is anextremely unlikely occurrence and so energy transfer must be induced by interactionswith other fluctuating magnetic fields. If the source of the other field is another nucleus,this interaction is called dipolar coupling and is discussed further in Section 1.12.1.The magnetic field fluctuations are produced by the movement or “tumbling” of thetwo nuclei. For the energy transfer to be efficient, this tumbling must occur at a ratenear to ω0. (Figure 1.4) Free water nuclei tumble much too rapidly for efficient T1relaxation through dipolar coupling and so have a long T1 value. Nuclei on stationarymacromolecules, large molecules such as proteins or lipids, tumble much too slowly andso also have a long T1. Water molecules near to macromolecules, the hydration layer,experience transient bonding to macromolecules which slows down their tumbling andresults in more efficient energy transfer and a shorter T1. T1 is also affected by B0 fieldstrength [15], and has been correlated with water content [16], neuron density, [17]myelin volume, [18] and iron content [19] in tissue.T2 relaxation results from processes which disrupt the phase coherence of spins.8This dephasing is mostly caused by variation in the local magnetic field of each nucleus,which changes the resonance frequency and causes a spin to gradually become out ofphase with its neighbours. This will happen more quickly if the differences in the localfields are large and static or only slowly fluctuating, so that the spins gain large phasedifferences. In rapidly tumbling molecules, for example in free water, the local fieldsexperienced average out and no consistent phase differences are built up. For this reason,free water has a long T2. Conversely, macromolecules have a very short T2 and hydrationwater has a T2 value somewhere in between. All T1 interactions also contribute to T2relaxation, which is why T1 values in tissue are longer than T2 values.So far we have only considered interactions between nuclei, however there can alsobe external contributions to the non-uniform local fields causing loss of phase coherence.We assign to these contributions another relaxation time constant T′2 . In combinationwith the innate relaxation time T2, this gives the measured transverse relaxation T ∗2 ,which is usually much shorter than T2.1T ∗2=1T2+1T ′2(1.14)T′2 may come from the inhomogeneity of the main magnetic field, B0 or suscepti-bility-induced field distortions, which can be caused by tissue boundaries or ferro-,para- or diamagnetic atoms. Susceptibility-induced changes to T ∗2 are the source offunctional MRI BOLD contrast, as oxyhemoglobin has a different susceptibility to de-oxyhemoglobin.In biological tissue, the T1 and T2 relaxation rates in each voxel are multi-exponentialand affected by many clinically relevant tissue properties. For this reason T1- and T2-weighted MRI scans, discussed in Section 1.7, can be used to visualise tissue morphol-ogy, quantify tissue volumes and diagnose many diseases.9Figure 1.5: A spin echo pulse sequence, showing the T ′2 decay being refocussedby the 180◦ RF pulse.1.4 Spin echo sequencesThe dephasing due to static magnetic field inhomogeneities (T′2 relaxation) can be re-covered for stationary spins using a Spin Echo (SE) MRI sequence. This sequence isformed of two RF pulses; most commonly a pulse with flip angle 90◦ and then, after adelay time τ , a pulse with flip angle 180◦. This process is illustrated in Figure 1.5. Aspin echo can in fact be formed of any pair of RF pulses; the original report of a spinecho was made by E.L. Hahn using two 90◦ pulses. [20]How the rephasing works can be illustrated by thinking of a single magnetisationvector tipped into transverse plane M⃗xy. After a time τ , the the vector will have spreadout into a fan shape, with some spins precessing faster than average, and some slower,due to their local ω0. The 180◦ pulse flips the fan over, so that the slow spins are at the“front” of the fan, and the fast spins at the “back”. After another time period τ , the fastspins will have caught up with the slow spins and the signal is back in phase. This only10works if the spins retain the same precession frequency over the whole sequence time(2τ), which is why spin echo sequences only rephase loss of coherence due to static orslowly fluctuating field inhomogeneities.A train of these spin echoes is called a CP sequence (after Carr and Purcell [21]) ifthe 180◦ pulse is applied in the same direction as the initial excitation pulse, by conven-tion the x-direction, and all pulses have the same phase. It is called a CPMG sequence(to include Meiboom and Gill [22]) if the 180◦ pulse is applied in a direction perpen-dicular to the excitation pulse (the y-direction). The CPMG sequence is less sensitive toerrors in RF flip angle than the CP sequence, because any slight difference from exactly180◦ will be corrected in every second echo. In both sequences, the 180◦ RF pulse isoften applied many times (for example 32 or 48 for Myelin Water Imaging (MWI) inSection 1.10), in order to measure the slow T2 decay.1.5 Gradient echo sequencesA Gradient Echo (GE) is a manipulation of the FID using dephasing and rephasinggradients. The FID, which would naturally experience T ∗2 relaxation, undergoes muchfaster dephasing due to the application of a magnetic field gradient. This dephasing isthen rephased by an equivalent gradient in the opposite direction. The gradient echohas a smaller amplitude than the signal at the beginning of the FID because T2 andT′2 processes are still occurring and are not rephased. This sequence is illustrated inFigure 1.6.The time between excitation and the centre of the echo, whether with a spin orgradient echo sequence is called the Echo Time (TE). The time between repetitions ofthe whole pulse sequence, for example between each phase encoding step, is called theRepetition Time (TR). Both TE and TR can be much shorter in a gradient echo sequencethan in a spin echo sequence, which allows for rapid imaging techniques such as MR11Figure 1.6: A gradient echo pulse sequence, showing dephasing and rephasing ofthe transverse magnetisation (Mxy) by gradients.angiography. The signal decay is determined by T ∗2 and so gradient echo sequencesrequire high B0 homogeneity to achieve an acceptable SNR.1.6 Spatial localisationFor an image to be produced, the collected signal must be spatially localised. In MRI, thelocalisation is achieved by slice selection, frequency encoding and phase encoding.5 Allthree of these techniques use magnetic field gradients superposed on B0. The directionof the magnetic field is always along zˆ, but the magnetic field strength can vary linearlyalong x, y and z. Gx, Gy and Gz are controlled by sets of gradient coils which are switchedon and off during the pulse sequence.5This is for a 2D MRI sequence. In a 3D MRI sequence, no slice selection is used, and phase encodingis performed in two directions. It is also possible to generate an image using only frequency encoding. Inthis case, gradients are applied in radial lines and then the image is generated using filtered back projectionfollowed by a 3D Fourier Transform.12B⃗z = (B0+ xGx+ yGy+ zGz)zˆ (1.15)Slice selectionA linear gradient is applied, usually in the z-direction although any plane can be cho-sen, which creates a linear variation in the Larmor frequency via Equation 1.7. If aslice-selective excitation B1 pulse is applied over a small range of frequencies ∆ω , onlynuclei in a small range of ∆z will be excited. The frequency spectrum of an RF pulse isapproximately the Fourier transform of its time dependence, and so to achieve a narrowbandwidth, a longer RF pulse is required.Often a long TR is required to recover the longitudinal magnetisation (Mz), par-ticularly for T2-weighted scans. Multi-slice acquisitions interleave acquiring data frommultiple other slices during the “dead-time” of the first slice. Non-consecutive slices arechosen for this, to minimise the “cross-talk” between adjacent slices.6 However, mag-netisation transfer effects from macromolecular 1H (see Section 1.11) may still occurbecause excitation pulses which are far off-resonance for aqueous 1H, will still excitemacromolecular 1H due to their much broader frequency spectrum.Frequency encodingUnlike slice selection, which is performed during excitation, frequency encoding is ap-plied while the signal is being received. A linear gradient is applied in a perpendiculardirection, often the x-direction, which changes the Larmor frequency of the signal re-ceived from nuclei along this gradient. Decomposition of the total received signal viaFourier Transform allows reconstruction of where along the x axis the signal came from.[13]6“Cross-talk” occurs between adjacent slices because the slice profiles created by the RF excitation arenot exactly rectangular and so overlap.13Phase encodingTo achieve spatial localisation in the third dimension, MRI utilises the phase of thereceived signal. A linear gradient in the y-direction is turned on for a short period oftime after excitation, but before signal collection. Nuclei along this gradient will gaindifferent amounts of phase, depending on their y position. One single measurement isnot enough to separate the phases from the total signal received, which is a sum of all ofthe individual signals. This step is therefore repeated with different strengths of phase-encoding gradient, often around 256 times for brain MRI, which determines the matrixsize in the y-direction.k-spaceThe signal from a single z slice, acquired after a phase encoding gradient Gy has beenapplied for time T , and a frequency encoding gradient Gx has been applied for time t(measured from the centre of the echo) can be written:S =∫ ∫Mxy(x,y)exp(iγGxtx)exp(iγGyTy)dxdy=∫ ∫Mxy(x,y)exp(ikxx)exp(ikyy)dxdy(1.16)Here γ is the gyromagnetic ratio as before, and Mxy(x,y) is the distribution of transversemagnetisation across the slice (which will depend on proton density, TE, TR etc.). Inthe second line, we have substituted kx = γGxt and ky = γGyT . kx and ky form a Fouriertransform pair with x and y and represent the spatial frequency of the image in the x andy directions. A pulse sequence will sample across k-space by varying kx and ky to buildup the image. In a simple spin echo sequence, each phase encoding step samples a linewith a fixed ky. All of the kx along that line are sampled during the frequency encodingtime range. The centre of k-space, the low spatial frequency data, is what determines the14contrast, whereas the periphery of k-space records the details of the image. k-space isa very useful way to visualise the acquisition of data before it is Fourier transformed tomake the image. Some advanced MRI techniques use complicated k-space trajectoriesto acquire data more efficiently.1.7 Clinical MRITR and TE can be varied to produce different contrasts in tissue. Generally the signalcollected from a voxel using a spin echo sequence is:S = M0 f (T1) f (T2)= M0(1− exp(−T RT1))exp(−T ET2) (1.17)where T1 and T2 vary in different tissues and pathologies. There are usually multiple T1and T2 proton pools in a single voxel of tissue. The three contrasts most commonly usedclinically are Proton Density (PD)-weighted, T1-weighted and T2-weighted.To achieve a PD-weighted image, f (T1) ∼ 1 and f (T2) ∼ 1 are required. Thisweighting is achieved with TR≥ 5T1, so that all of the longitudinal magnetisation (Mz)has recovered, and TE<<T2 so that not much transverse dephasing has occurred. AT1-weighted image is produced using TE<<T2, to minimise any T2 weighting, althoughthere will still be some. It also requires TR∼T1 for the tissues of interest, to maximiseT1 contrast. Equivalently, a T2-weighted image is produced using TR>>T1, to minimiseany T1 weighting, and TE∼T2 for the tissues of interest, to maximise the contrast.It should be noted that both T1- and T2-weighted images still have significant PDweighting. In broad strokes, T1-weighted scans provide the best anatomical definition,T2-weighted scans highlight pathology such as tumours or Multiple Sclerosis (MS) orSpinal Cord Injury (SCI) lesions and PD-weighted scans provide the relative water con-15Figure 1.7: Commonly used contrasts in MRI: T1-, T2- and PD-weighted scans,and their required TE and TR times. Figure adapted from one courtesy ofAllen D. Elster. [3]tent of different tissues.1.8 Other rapid imaging sequences used in this thesis1.8.1 EPIIn an Echo Planar Imaging (EPI) acquisition, multiple k-space lines are acquired for ev-ery excitation of the signal. During one repetition time, the frequency encoding gradientis applied in one direction and then the opposite direction multiple times, traversing kx inthe positive and then negative direction. While the frequency encoding gradient is beingreversed, phase encoding “blips” are applied to shift the acquisition to a new ky line.If the whole of k-space is acquired in one repetition the sequence is called single-shotEPI, if k-space is acquired in 2 or more repetitions, it is multi-shot EPI. This is a veryfast acquisition method, and it is versatile as any desired contrast can be generated using16preparation pulses before the EPI read-out. The limiting factor for the speed of an EPIacquisition is the gradient slew-rate, which determines how quickly strong gradients canbe switched on.1.8.2 RARERapid Acquisition with Refocused Echoes (RARE) is a fast spin echo sequence which,like EPI, acquires more than one k-space line per repetition. Multiple 180◦ pulses areapplied to create many spin echoes. A different phase-encoding is applied to each echoto collect a different k-space line. The increase in acquisition speed is proportional to thenumber of echoes or k-space lines per repetition e.g. 3 echoes = 3x faster. This numberis called the echo train length or turbo factor.1.8.3 FLASHFast Low-Angle SHot (FLASH) is a steady-state gradient echo sequence which accom-modates a very short repetition time using a flip angle < 90◦. The optimal angle formaximum signal at a given repetition time is called the Ernst angle, given by:αE = cos−1 (exp(−T R/T1)) (1.18)In practice, an angle in the range 30-60◦ is used and the system quickly reaches a steady-state, where the longitudinal magnetisation reaches an equilibrium constant value at ev-ery echo. This sequence also commonly employs spoiling gradients to disrupt transversecoherences.1.8.4 IR-TFE/MPRAGEInversion recovery (IR)-Turbo Field Echo (TFE) and Magnetization Prepared - RApidGradient Echo (MPRAGE) are both 3D IR-prepared gradient echo sequences. They17consist of a non-slice selective 180◦ inversion pulse followed by many rapidly acquiredgradient echoes using a < 90◦ flip angle (a FLASH acquisition). This produces a T1-weighted image.1.9 Quantitative MRIThe signal in each voxel of a clinical T1-, T2- or PD weighted MRI image is affected bymany MRI sequence and data processing variables as well as intrinsic tissue properties.For this reason, raw signal values from clinical images are generally not examined andconventional MRI techniques rely on contrast between nearby regions to detect patholo-gies and visualise structures; subtle global changes in large regions of tissue may not bedetectable. Another issue with conventional MRI is biological non-specificity; a hyper-intensity in a T2-weighted image of brain white matter could be caused by inflammation,edema, demyelination, axonal loss, a combination of these processes, or something elseentirely.Quantitative MRI techniques measure meaningful tissue variables in physical units.The fact that these variables are quantitative does not necessarily make them biologicallyspecific, but it does mean that they can be compared between tissue regions, time-pointsand subjects, and normative healthy values can be established. In this thesis I will discussfour promising quantitative Magnetic Resonance Imaging (qMRI) techniques and theirapplication to the study of white matter microstructure.1.10 Myelin water imagingIn tissue, the T2 signal decay from an MRI voxel (usually around 1 mm3 in size) ismulti-exponential because it contains 1H in many different environments. For examplein white matter, 1H can be found inside cells, outside cells, in free water, and trappedbetween myelin bilayers. The structure of myelin is discussed further in Section 2.2 but18Figure 1.8: Multi-echo spin echo pulse sequence, used to measure the T2 decaycurve for MWI.an important fact for the purposes of MWI is that myelin bilayers are tightly wrapped.Water molecules are around 3A˚ in diameter while the intra-period line in myelin, wherethe water is trapped, is approximately 30A˚ wide, [23] meaning that 1H have very con-strained motion and therefore a short T2 relaxation time (see Figure 1.4). This short T2signal can be isolated by measuring the T2 signal decay, using a train of spin echoes.The original acquisition for MWI used a multi-echo spin-echo CPMG sequence,slightly modified from its simplest form (Figure 1.8) to reduce stimulated echoes due toimperfect RF pulses with the addition of “crusher” gradients. This sequence is the goldstandard for acquiring MWI data, but has a very long acquisition time.1.10.1 Acquisition accelerationCurrently used in vivo implementations of MWI achieve whole brain coverage in rea-sonable scan times by accelerating the acquisition using both GRadient And Spin Echo(GRASE). This sequence sandwiches each spin echo in the multi-echo train betweentwo gradient echoes, thereby acquiring 3 k-space lines per excitation (Figure 1.9). In19Figure 1.9: GRASE pulse sequence, used to measure the T2 decay curve for MWI.Each spin echo is now between two gradient echoes.this way, the acquisition time is reduced by a factor of 3. Naturally, the gradient echoeswill have T ∗2 - rather than T2-weighting and so they are used to fill lines in the peripheryof k-space, while the spin echoes are used to fill the centre, so that the image as a wholeretains a mostly T2 weighting. The 3D GRASE implemented by Prasloski et al. [24] isthe MWI acquisition sequence used throughout this thesis.Another acceleration technique has recently been proposed which uses compressedsensing to accelerate the multi-echo spin-echo sequence. [25] This removes the need forgradient echoes which introduce some T ∗2 -weighting to the MWI data.Multicomponent Driven Equilibrium Single Pulse Observation of T1 and T2 (mcDESPOT)is an alternative method to estimate the short-T2 myelin water component in white mat-ter. [26] The acquisition uses rapid spoiled gradient-recalled echo and balanced steady-state free precession sequences to obtain T1 and T2 decay data which is then fit witha two-pool tissue model including exchange to find the fraction of the fast T2 compo-nent, assumed to be myelin water. This method has a fast acquisition, but consistentlyprovides higher Myelin Water Fraction (MWF) values than a multi-echo spin echo ap-20proach, [27] and the fitting has been found to be affected by the particular acquisitionscheme and algorithm search parameters used. [28]1.10.2 MWI analysisStimulated echoes are a problem in GRASE acquisition. They occur when longitudinalmagnetisation (Mz) remains after the application of a refocusing pulse which is not ex-actly 180◦. This effect is visible as a “zig-zag” pattern in the T2 decay curve, with anincrease in signal at “even” echoes and a decrease at “odd” echoes. To mitigate this forMWI, an extended phase graph algorithm is used to model the longitudinal and trans-verse magnetisation for a range of flip angles α1 < 180◦. [29] The true flip angle isdetermined by comparing this simulated data with the real decay using Non-NegativeLeast Squares (NNLS). The true flip angle is used to correct the decay curves, which arethen fit with multiple T2 components using the NNLS algorithm proposed by Whittalland MacKay [30] in 1994. The signal at the jth echo is given by:S(t j) =Ni∑i=1Ai exp(−t jT2,i)+δ j (1.19)j iterates over the number of echoes (N j), i sums over the chosen number of T2 distri-bution points (Ni), and δ j is the noise at each echo time. These N j linear equations aresolved by χ2 minimisation, subject to two constraints:1. Each contribution Ai > 0 (which is why it is called non-negative least squares).2. The T2 distribution is smooth, ensured by including a regularisation term in theNNLS minimisation.In the standard NNLS analysis used in this thesis, Ni = 40 logarithmically spacedbetween 15 ms - 2 s. The benefit of this method is that the number and T2-range of 1H21Figure 1.10: The T2 distribution fit using NNLS and how it is used to calculate theMWF.pools in the tissue is not assumed, instead a T2 spectrum is found which can then bedivided into biologically relevant pools.In each voxel the fraction of the total signal below the myelin water T2 cut-off (T2<40 ms in the brain in vivo at 3 Tesla) is calculated, and labelled the MWF. MWIanalysis also provides the Intra-/Extracellular Water geometric mean T2 (I/EW gmT2),which is the amplitude-weighted mean of the logarithm of the T2 distribution for theintra/extra-cellular water pool. [31] I/EW gmT2 has been found to increase with tissuedamage. [32–35]MWF has good scan-rescan reproducibility (coefficient of variation 4% in cerebralwhite matter) [36] and is currently used to study a wide range of tissues and diseasesincluding MS, neuromyelitis optica spectrum disorder, schizophrenia, Niemann-Pickdisease, prenatal alcohol exposure, amyotrophic and primary lateral sclerosis, Krabbedisease, autism, stroke, and traumatic brain injury. [32, 37–49] MWF has been com-pared with parameters from other myelin sensitive techniques including diffusion tensor22imaging and magnetization transfer. [1, 50, 51]Some limitations to this MWI analysis include:1. 1H exchange between compartments, particularly the intracellular and myelin wa-ter compartments, may be occurring on relevant timescales and decreasing MWFmeasurements, although this is contested. [52] Exchange is likely to have a largereffect in areas with smaller axons and thin myelin sheaths. [53]2. To reduce acquisition time, TR is usually too short to eliminate all T1-weighting.3. RF flip angle inhomogeneity, causing the stimulated echo “zig-zag” in the first 6or 7 echoes of the decay curve, particularly affects MWF and MWF T2 estimatesbecause the MWF signal is only present for the first few echoes in the decay. Ifall of the odd echoes in this range are underestimated, both MWF and MWF T2will be underestimated. [54]4. Regularisation applied during the NNLS fitting may result in an underestimatedMWF due to smoothing. [55]1.11 Magnetisation transfer imagingMacromolecular 1H are generally invisible in conventional MRI due to their fast T2 de-cay, which causes signal loss before spatial encoding can be performed. MagnetisationTransfer (MT) is a method to gain information about these “invisible” 1H. To calculatethe Magnetisation Transfer Ratio (MTR), images are obtained with (S+) and without(S0) an off-resonance pre-pulse:MTR =S+−S0S0(1.20)23Figure 1.11: The off-resonance pre-pulse applied to the broad non-aqueous spec-trum and subsequent magnetisation transfer to the aqueous 1H.Macromolecular 1H have a broad resonance lineshape and so the off-resonance pre-pulse preferentially saturates the magnetisation of the macromolecular 1H. This satu-ration is exchanged, via chemical processes and cross relaxation, with water 1H, andresults in an MRI contrast which is sensitive to many aspects of the tissue microstruc-ture including myelin content. The primary mechanism for macromolecule-to-watermagnetisation exchange is 1H exchange between water and hydroxyl protons on lipidheadgroups. [56] This is efficient in myelin because it comprises many non-aqueous 1Hon lipid chains in close proximity to water and so myelin has a high MTR. HoweverMTR is also affected by the density of other tissue components such as axons and mi-croglia, as well as tissue water content, which reduces the myelin specificity of the MTmethod and therefore its utility as a myelin biomarker. [57]1.12 Inhomogeneous magnetisation transfer imagingInhomogeneous Magnetisation Transfer (ihMT) is a novel magnetic resonance imaging(MRI) technique sensitive to myelin density which is based on similar principles to MT.The inhomogeneous Magnetisation Transfer Ratio (ihMTR) is defined as the difference24between the average water signal attenuation with off-resonance pre-pulses at singlepositive and negative offsets (S+ and S−) and the attenuation with a pre-pulse of thesame total power but split between positive and negative offsets (S± and S∓), normalisedby the signal with no pre-pulse (S0):ihMTR =S++S−−S±−S∓2S0(1.21)The double subtraction in the numerator is included to correct for any asymmetry of themacromolecular line. [58] Much of the ihMTR literature, including its original defini-tion, does not include a 2 in the denominator. [58–67] In this thesis I have chosen tobalance the double subtraction by dividing by 2, in order to make the ratio comparableto MTR. The ihMTR in Equation 1.21 reflects the fractional decrease in signal whensingle-frequency off-resonance saturation is replaced by a dual-frequency off-resonancesaturation. This definition of ihMTR is in line with the definition of MTR, which is thefractional decrease in signal when single-frequency off-resonance saturation is applied.[68, 69]Some of the first experiments using ihMT showed that heat-denatured ovalbumin,agarose and gelatin produce the same decrease in aqueous signal for the S+ and S± ex-periments, and so the ihMTR= 0. However samples containing lipid bilayers includingbrain white matter in vivo, amphiphilic lipids, lamellar phospholipids with cholesterol,bovine spinal cord, and hair conditioner have more aqueous signal attenuation for S±and a measurable ihMTR. [59, 70] ihMTR has been applied to study demyelination inamyotrophic lateral sclerosis (ALS), [71], MS brain [72, 73] and spinal cord [74] anddepression [75, 76].To understand the contrast mechanism of ihMT we must first discuss dipolar cou-pling and Provotorov theory.25Figure 1.12: Dipolar coupling between two spins.1.12.1 Dipolar couplingEach nuclear spin generates its own small magnetic field. Dipolar coupling is the inter-action of the magnetic field from one nuclear spin (spin 1) with the magnetic field of aneighbouring nuclear spin (spin 2). This interaction is mutual; spin 2 also experiencesthe magnetic field from spin 1. The Hamiltonian for this interaction is:ĤD =µ0γ1γ2ℏ4π|−→r12|3(Ŝ1 · Ŝ2−3(Ŝ1 · r̂12)(Ŝ2 · r̂12))(1.22)where µ0 is the vacuum permeability (= 4π × 10−7 H/m), γ1 and γ2 are the spins’ gy-romagnetic ratios, −→r12 is the vector joining the two spins and r̂12 is a unit vector in thedirection of −→r12. The prefactor is often combined into a dipole-dipole coupling constant:b12 =µ0γ1γ2ℏ4π|−→r12|3 (1.23)26The energy is minimised when the two spins are pointing in the same direction along−→r12, i.e. head to tail, and −→r12 lies parallel or antiparallel to B0.For nuclei in a strong magnetic field ĤZ>>ĤD and so a secular approximation ofĤD can be used. This ignores terms which are off-diagonal in the matrix of ĤD repre-sented in the eigenbasis of ĤZ , leaving only terms which connect degenerate or near-degenerate eigenstates of ĤZ . For two identical spins, under the secular approximation,Equation 1.22 becomes:ĤD =12(3cos2 θ12−1)b12(Ŝ1 · Ŝ2−3Ŝ1zŜ2z)(1.24)where θ12 is the angle between −→r12 and B⃗0. At the magic angle, θ12 = 54.7◦, the dipolarcoupling strength is zero.In isotropic liquids, the effect of dipolar coupling is motionally averaged and ĤD≃ 0.However in an anisotropic liquid, where there is a preferred molecular orientation, in-tramolecular dipolar coupling does not average out. This is the case in the myelin sheath.The myelin sheath is composed of concentric lipid bilayers surrounding a central axon(described in more detail in Section 2.2). Within each bilayer, 1H in methylene groupson lipid acyl chains experience strong homonuclear dipolar couplings. [77, 78] Dueto rapid lateral lipid diffusion, the contribution from intermolecular dipolar couplingscan be neglected. The lipids’ rapid spinning about their long axes reduces the effectof intramolecular couplings and projects these interactions along a symmetry axis per-pendicular to the bilayer. The myelin bilayer can therefore be modelled as a system ofstrongly coupled methylene spin-1/2 pairs, weakly coupled to adjacent methylene pairs.Using a homonuclear spin-1 model of two isolated protons results in a spectrum con-sisting of a superposition of Gaussian-broadened doublets with splitting determined bythe dipolar coupling strength. [68, 79, 80] This splitting determines the spectral width27of the lipid signal which can be quantified using the second moment, M2.M2 =∫ ∞−∞ω2 f (ω)dω∫ ∞−∞f (ω)dω(1.25)Equation 1.24 suggests that M2 should have a dipolar contribution:M2 ∝ (3cos2 θ12−1)2 (1.26)1.12.2 Provotorov theoryThe picture becomes more complicated when RF irradiation is applied. Now the fullHamiltonian for the spins is:Ĥ = ĤZ + ĤD+ ĤRF (1.27)Each term has a characteristic energy: ℏω0, ℏωD and ℏω1, where ωD is the averagedipolar coupling strength or local field strength. ĤRF does not commute with ĤZ or ĤDso there is no simple way to determine the evolution of the states. Instead, differentapproximations are appropriate in different regimes. Bloembergen, Purcell, and Pound(BPP) used perturbation theory to model the effect of extremely weak RF irradiation.[81] Redfield theory uses the concept of spin temperature in a thermodynamic ensembleto predict the behaviour of solids under strong RF irradiation. [82]Provotorov theory is an extension of Redfield theory to the case of weak RF irra-diation, usually ω1<<ωD. [83] In this theory, the non-aqueous Zeeman and (secular)dipolar Hamiltonians commute and are therefore separable. This allows the definition oftwo separate thermodynamic reservoirs associated with the non-aqueous spins [83–85]:281. The Zeeman reservoir, with associated magnetization M⃗0 (the polarization of spinsin the B⃗0 field) and nuclear spin relaxation rate R1.2. The dipolar reservoir, with associated order (the polarization of spins within theirlocal dipolar fields) and nuclear spin relaxation rate R1D.When single-offset RF irradiation is applied, the additional RF Hamiltonian term in-troduces a coupling between the two reservoirs, allowing magnetization to flow betweenthem. However, for dual-offset RF irradiation, the two reservoirs are separable and re-main decoupled. By measuring the difference in signals between single and dual offsetexperiments, ihMTR is sensitive to the transfer of magnetization between the dipolar andZeeman reservoirs. This transfer is dependent on R1D; if R1D is fast, rapid relaxation ofthe dipolar order means little magnetization is left to flow back into the Zeeman reser-voir making the saturation efficiency of the single offset frequency experiments similarto the dual offset frequency experiment, and reducing the ihMTR. On the other hand,if R1D is slow, there will be much less relaxation in the dipolar reservoir, resulting inless efficient RF saturation of the aqueous signal in the single-offset case and a higherihMTR. [70] Quantitatively, ihMTR is affected by the RF frequency offset, pulse power,and lipid lineshape. [60, 66]Unlike conventional MT, which is sensitive to 1H on a variety of macromolecules,ihMT is only sensitive to 1H experiencing dipolar coupling interactions with sufficientlylong dipolar order relaxation time (T1D = 1/R1D) in a motion-restricted environment suchas a lipid membrane.1.12.3 T1D filtering and “boost” effectT1D-filtering is an acquisition method which can be used to restrict the ihMTR to onlyinclude signal from tissue components with very long T1D. This is achieved by increas-29Figure 1.13: Two different dual off-resonance pre-pulse application methods. Thecosine-modulated pre-pulse applies both the +∆ f and −∆ f frequencies atonce, and so there is no T1D-filtering. The frequency alternating methodallows for T1D-filtering controlled by the delay time ∆t.ing the delay time (∆t) between the alternating positive and negative frequency offsetpre-pulses so that induced dipolar order with short T1D decays between each pulse. Onthe other hand, a cosine-modulated pre-pulse applies both off-resonant frequencies at thesame time and so signal from protons with a shorter T1D (which is still long enough toproduce ihMTR signal) is also included in the ihMTR. T1D-filtering has been suggestedto increase the myelin specificity of ihMT, as protons on myelin lipids have a long T1D.[66]It has also been found that decreasing the pre-pulse saturation time, and increasingthe concentration of power deposition (B1rms) greatly increases the collected ihMTRsignal; this is called the “boost” effect. [86, 87]1.12.4 Reducing sensitivity to T1 and B1 inhomogeneitiesThe simple ihMTR metric given in Equation 1.21 is sensitive to T1, B1 and other as-pects of sequence design. Recent improvements to ihMT acquisition and analysis have30attempted to remove these dependencies to achieve a more reproducible metric. Thesimplest change is to quantify the inverse of the subtraction instead [87]:ihMTRinv = S0(1S±+1S∓− 1S+− 1S−)(1.28)This makes the metric approximately proportional to the pre-pulse power depositionand so B1 inhomogeneities can be more easily accounted for. This metric is used inSection 5.3. A further improvement not employed in this thesis, but interesting for futureresearch, is the ihMTsat approach. This is an acquisition and analysis procedure similarto MTsat [88], which involves obtaining an additional S0 image with a higher readout flipangle to remove T1 dependency. [89] This metric has also been called “qihMT”. [90]1.13 Diffusion imagingThe diffusion of water molecules in tissue can be measured with a Diffusion-WeightedImaging (DWI) MRI sequence. This technique was first developed by Stejskal and Tan-ner in the 1960s [91] and uses two identical diffusion-sensitising magnetic field gradientsplaced either side of the refocusing 180◦ RF pulse.Stationary protons end up in phase, as they experience symmetric dephasing andrephasing from the two gradients. However, protons which diffuse in the direction ofthe gradients experience different field strengths during each and so are not rephased,resulting in signal loss. The diffusion-sensitising gradients can be applied in multipledirections to characterise the diffusion profile in 3D.Diffusion, or Brownian motion, occurs due to the random motion of molecules withkinetic energy due to their non-zero temperature (T > 0 K). In a homogeneous fluid, the31Figure 1.14: Diffusion-weighted MRI pulse sequence.graph of probability against displacement for diffusion is approximately Gaussian:P(x) =1σ√2πexp(−x22σ2)(1.29)Here we are choosing x as the diffusion direction of interest. The mean-squared dis-placement in 1D is given by Einstein’s equation: [92]x2 = 2Dt (1.30)D is the Fick diffusion coefficient, which is higher for more mobile molecules, and t isthe time from the start of diffusion. Substituting in σ2 = x2, this givesP(x, t) =1√4πDtexp(−x24Dt)(1.31)32which is called the diffusion propagator.The phase shift φ(x) accrued by a spin at position x, during diffusion sensitisationwhich occurs over a time ∆+δ (see Figure 1.14), for a gradient strength Gd is given by:φ(x, t) = iγ∫ ∆+δ0Gd(t)x(t)dt (1.32)To calculate the signal loss after a diffusion time ∆+δ , an integral over x and t mustbe performed:S = S0∫ ∆+δ0∫ ∞−∞P(x, t)exp(iφ(x, t))dxdt= S0 exp(−γ2G2dδ 2(∆−δ3)D) (1.33)The constants are often abbreviated to b= γ2G2dδ2(∆−δ/3), which can be varied usingthe pulse sequence parameters Gd , δ and ∆. A higher b-value improves the diffusioncontrast but also results in a lower SNR. For DWI in the brain, b ∼ 1000 s/mm2 isusually employed. A b= 0 scan is also obtained to determine S0. Maximising G, withinsafety limits, allows for a smaller δ for the same b and therefore reduces the T2 decaywhich occurs before the signal is recorded. However some T2-weighting will still bevisible on the image termed “T2 shine-through”.For water at 37°C, D = 3× 109 m2/s and in a typical DWI scan t = 10− 50 ms.[93] This means water molecule displacement during diffusion-sensitising will be onthe order of micrometers, using Equation 1.30, and so DWI can measure properties oftissue architecture at the length scale of cellular structures like axons and myelin sheaths.In white matter, diffusion is anisotropic because there are more barriers to water dif-fusion perpendicular to the fibers from longitudinally oriented structures such as myelin,axon membranes, microtubules and neurofilaments. Water diffusion will occur morequickly along the length of the axon bundles than perpendicular to them. Diffusion in33grey matter is mostly isotropic as there is less coherent alignment of the componentcells.Pathology may either decrease or increase the diffusion of water in tissue. Injuredtissue with disrupted microstructure and cell necrosis generally has more extracellularwater and so freer diffusion and a higher D. Acute ischemia, infections and areas of highcellularity in tumours can have a lower D.1.13.1 Diffusion Tensor ImagingDiffusion Tensor Imaging (DTI) is frequently used to assess white matter microstructure.[94] The method assumes anisotropic Gaussian diffusion and fits a symmetric 3× 3diffusion tensor to the DWI data from each voxel (Equation 1.34). This fitting requiresat least 6 non-collinear diffusion gradient directions; in practice usually 30 or more areused.D =Dxx Dxy DxzDyx Dyy DyzDzx Dzy Dzz (1.34)which can be diagonalised to giveD =λ1 0 00 λ2 00 0 λ3 (1.35)The three eigenvalues are used to calculate the four scalar DTI parameters commonlyreported: Axial Diffusivity (AD), the rate of diffusion along the direction of fastestdiffusion, Radial Diffusivity (RD), the rate of diffusion perpendicular to the direction offastest diffusion, Mean Diffusivity (MD), the mean diffusion, and Fractional Anisotropy34(FA), the fraction of the tensor’s magnitude which is due to anisotropic diffusion.AD = λ1 (1.36)RD =λ2+λ32(1.37)MD =λ1+λ2+λ33(1.38)FA =√32√(λ1−MD)2+(λ2−MD)2+(λ2−MD)2λ 21 +λ 22 +λ 23(1.39)FA ranges from 0 for isotropic diffusion, to 1 for completely anisotropic diffusion.In brain white matter, which is composed of tightly packed coherently aligned myeli-nated axons, diffusion is fairly restricted except parallel to the axons giving a mean FA ofaround 0.3. Diffusion is more isotropic in grey matter and completely isotropic in Cere-brospinal Fluid (CSF). [93, 95] DTI has a good scan-rescan reproducibility (coefficientof variation 2% in cerebral white matter). [96]FA increases and MD decreases during development, and the opposite trends areseen in older age. [97] It has been shown that AD decreases in areas of axon degenera-tion and RD increases in areas of demyelination. [98–100] However, this direct assign-ment may be too simplistic in pathological tissue where many competing tissue changessimultaneously occur, affecting DTI parameters. [101]One issue in DTI analysis of both healthy and pathological white matter is the“crossing fiber problem”. It has been estimated that, with standard DTI voxel sizes,66-90% of all white matter voxels in the brain contain converging, diverging or cross-ing fibers. [102] Using DTI analysis to fit one direction of fastest diffusion in a voxel35containing perpendicular fibers results in a primary eigenvector which does not alignwith either of the underlying fibre directions and an FA much lower than for either tractindividually. In central nervous system pathology, and particularly in MS, DTI measure-ments are also affected by edema and inflammation/cellularity, which increase the highand low diffusivity isotropic diffusion respectively. Both processes reduce FA and somight be misinterpreted as demyelination or axon damage.To accurately identify specific biological changes using DWI, more sophisticatedanalysis techniques are necessary. These fall broadly into two categories; signal repre-sentations and tissue models. Signal representations aim to fit the complicated diffusionprofile in each voxel without assumptions about the underlying microstructure. Thefitting is applicable to any tissue type, but the parameters derived are not specific tobiological components and so can be hard to interpret clinically. On the other hand,tissue models compartmentalise water molecules into discrete biological environmentswith assumed characteristics. These pre-chosen components are fit to the DWI data andused to estimate clinically relevant parameters, such as axon diameter, or the fractionof signal from water trapped in cells. This method naturally entails some simplificationof the true biological complexity but the aim is to accurately represent all of the rele-vant aspects of the tissue microstructure. The results from tissue models are easier tointerpret but rely on the model being accurate and applicable to the tissue or pathologyof interest. Many of the signal representations and tissue models currently in use havebeen shown to successfully address the crossing fibre problem with the use of high an-gular resolution DWI and more complicated fitting, however very few of them tackle theissues of edema and cellularity in areas of central nervous system pathology.361.13.2 Diffusion Kurtosis ImagingThe assumption of Gaussian diffusion underlying DTI fitting is only true for free diffu-sion in liquids. In tissue, where there are different water compartments, barriers to diffu-sion, and chemical exchange occurring, the diffusion profile is not Gaussian. The devia-tion from a Gaussian profile becomes more noticeable for stronger diffusion-weighting(higher b-values). Diffusion Kurtosis Imaging (DKI) is a signal representation whichuses a Taylor expansion of the diffusion equation to include a term for non-Gaussiandiffusion:S = S0 exp(−bD+ b2D2K6)(1.40)K is a dimensionless parameter which statistically quantifies the shape of the diffusionprobability distribution. If the diffusion is Gaussian, K = 0. More peaked distributionshave a positive kurtosis (K > 0, Figure 1.15), more flattened distributions have a negativekurtosis (K < 0). Biological tissues have a positive kurtosis, as 1H in tissue is less likelyto travel far in a given time period than 1H in free water.1.13.3 Diffusion Basis Spectrum ImagingDiffusion Basis Spectrum Imaging (DBSI) is a tissue model which was developed toaddress the issues which arise when using DTI to analyse brain diffusion data in MS,containing crossing fibres, edema and inflammation. In this complicated model, the sig-nal from each voxel is divided into isotropic and anisotropic components. The isotropiccomponent is modelled as a continuous spectrum of diffusion tensors with a range ofdiffusivities. The spectrum is often divided up into a fraction with D < 0.3 µm2/ms,assumed to be the water trapped inside cells, and an unrestricted fraction with D > 0.3µm2/ms, assumed to be the extracellular water. The anisotropic component is sepa-rated into up to three discrete axially symmetric anisotropic tensors. This is assumed to37Figure 1.15: The diffusion propagator for Gaussian diffusion compared to diffu-sion in biological tissues, where the kurtosis K > 0.contain the signal from water inside and adjacent to the axons.Sk =Naniso∑i=1fi exp(−bkRDi)exp(−bk(ADi−RDi)cos2(ψik))︸ ︷︷ ︸Naniso anisotropic diffusion tensors+∫ ∞0f (D)exp(−bkD)dD︸ ︷︷ ︸spectrum of isotropicdiffusion tensors(1.41)Here bk is the b-value of the diffusion-weighting and S is the measured diffusion-weighted signal, corresponding to the k-th diffusion gradient. Naniso is the number ofidentified anisotropic tensors, which is determined separately for each voxel. ψik isthe angle between the k-th diffusion gradient and the i-th anisotropic tensor. ADi and38RDi are AD and RD for the i-th anisotropic tensor assuming cylindrically symmetrictensors. fi is the fraction of the signal from the i-th anisotropic tensor, termed the i-thfibre fraction. And finally, f (D) is the isotropic diffusion spectrum. There is a two stepfitting method used to determine the parameters in Equation 1.41:Step 1 In this step, Equation 1.42 which is a slightly modified version of Equation 1.41is used to find Naniso and ψik. A diffusion basis set of N = 95 tensors is chosen, withknown θik, evenly distributed around the sphere. Initially ADi, RDi and diso are ran-domly selected and an NNLS fit to the data is performed to obtain the signal contribu-tions, ci, from each of the 95 diffusion basis tensors. The NNLS fitting is constrainedso that fiber crossings are sparse and the signal contributions are non-negative. Then thefound ADi, RDi, diso and ci are analysed using a generalised pattern search algorithmwhich chooses optimised new values for ADi, RDi and diso until the solution convergesat the global minimum. The optimised ci are then used to derive the optimal Naniso andψik.Sk =95∑i=1ci exp(−bkADi)exp(−bk(ADi−RDi)cos2(θik))︸ ︷︷ ︸discrete diffusion basis set+ exp(−bkdiso)︸ ︷︷ ︸averaged effect of theisotropic diffusion(1.42)Step 2 The second step involves using the Naniso and ψik from Step 1, applying themto Equation 1.41, and fitting to find ADi, RDi, and fi, as well as the isotropic diffusionspectrum f (D). Initially the isotropic spectrum is discretized. Similarly to Step 1, ADiand RDi are randomly selected and NNLS analysis is used to fit fi and f (D). In thisstep the conditions on NNLS fitting are again that signal contributions must be non-negative, and also that signal must have finite energy which retains numerical accuracy39Figure 1.16: The difference between DTI and DBSI fitting of diffusion data. Re-produced with permission from Cross and Song [4].of the solution while preventing overfitting to noisy data. A generalised pattern searchalgorithm is used to recursively choose new ADi and RDi until the global minimum isreached. Once both Step 1 and Step 2 are completed Naniso, f (D) and ψik, fi, ADi andRDi for each anisotropic tensor component are determined.A comparison between DTI and DBSI modelling of the same voxel can be seen inFigure 1.16. DBSI has been validated using phantoms [4, 103], simulations [104] andimmunohistochemistry [4, 103, 105, 106] and has been applied to the study of MS [105],HIV [107] and SCI [108].The fraction of the signal in the anisotropic components gives the fibre fractionwhich is thought to be related to the axons in each voxel. The isotropic fraction is di-vided into low, medium and high diffusivity giving the restricted, hindered and isotropicfractions respectively. The restricted fraction is thought to relate to the water trapped40inside cells.1.14 Validating quantitative MRI techniquesWhen a new qMRI contrast mechanism is developed, which purports to be sensitive orspecific to a biological tissue characteristic, it is vital that this claim is validated. Tissuemicrostructure is very complicated and many biological processes both in vivo and exvivo can change qMRI contrast. The situation becomes more complicated in pathology,where models for tissue structure and relaxation developed for healthy tissue may beinappropriate. Various methods are used to validate the biological specificity of potentialqMRI biomarkers. Simulations can be used to model the effect of tissue components onMRI signal, for example iron and myelin on T ∗2 or diffusion in axons. Alternatively theqMRI metric can be compared to histological staining, which is considered to be thegold standard for assessing specific tissue components and pathological processes.Histological stainingLuxol Fast Blue (LFB) is a histology stain designed to highlight myelin phospholipids.It can be used to validate the specificity of myelin-sensitive MRI metrics. Immunohisto-chemical stains for activated microglia and macrophages can be used to correlate MRIbiomarkers with inflammation. In this thesis we used Class II Major Histocompatibil-ity Complex (Class II MHC) which comprises cell surface receptors used by antigen-presenting cells and Cluster of Differentiation 68 (CD68) which is a lysosomal/endoso-mal glycoprotein expressed in microglia and macrophages.Most of the myelin metrics explored in this thesis have been validated as sensitiveto myelin using histology: MWI has been validated in both human brain and spinalcord, [109–111] ihMT has been validated using fluorescence imaging of geneticallymodified proteolipid protein Green Fluorescent Protein (plp-GFP) mice, [61] MTR has41been validated in both human MS and animal brain tissue using LFB, [112, 113] andDWI and kurtosis metrics have been compared to histology for myelin and axons inmany different studies (see Lazari and Lipp [114] for an excellent review).42Chapter 2Myelin in the central nervoussystemMost of the qMRI techniques used in this thesis are designed to be sensitive to myelin.In this chapter I will briefly describe the biological background for myelin qMRI in thebrain and spinal cord.2.1 Central nervous systemThe central nervous system is composed of nerve cells or neurons, supported and pro-tected by neuroglia. A neuron has four main parts (Figure 2.1). The cell body containsthe nucleus and metabolic components of the cell. The dendrites are short branchesextending from the cell body which receive signals from other neurons. The axon is along fiber which can be up to 1 m in length, ending in the axon terminals, which are incontact with dendrites of other neurons. [115]Nerve impulses, or action potentials, travel along the axon through depolarisationand repolarisation of the negative electrical charge inside the cell membrane. The pro-cess is mediated by sodium and potassium ions which are maintained in non-equilibrium43Figure 2.1: The structure of a neuron, from OpenStax Anatomy and Physiology[5], under Creative Commons Attribution 4.0 International (CC BY 4.0).concentrations inside and outside of the axon via sodium-potassium pumps in the cellmembrane. Myelin wraps concentrically around axons, insulating the intracellular fromthe extracellular ions, which forces the axon potential to jump between gaps in themyelin sheath, called the nodes of Ranvier. This is saltatory conduction and it increasesthe speed of action potentials travelling along the neuron to 10− 100× that seen in anunmyelinated axon.Over 60% of cells in the brain are neuroglia, which support the function of the neu-rons (Figure 2.2). Astrocytes are star-shaped cells which wrap around the neurons form-ing a supporting network. They also regulate ion concentrations and form the blood-brain barrier along with endothelial cells. Oligodendrocytes produce myelin. Microgliaare small cells which protect the nervous system from pathogens by engulfing and de-stroying foreign microbes and cellular debris.44Figure 2.2: The environment of white matter, including oligodendrocytes, astro-cytes and microglia, from OpenStax Anatomy and Physiology [5], under Cre-ative Commons Attribution 4.0 International (CC BY 4.0).The brain and the spinal cord are classified into white matter and grey matter, sur-rounded by CSF. White matter is composed of myelinated axons, oligodendrocytes,and other glial cells. Its white colour comes from the high lipid-content of the myelinsheaths. Grey matter is mainly composed of neuronal cell bodies, along with glial cellsand capillaries. CSF is a clear fluid which protects the brain and spinal cord from me-chanical shocks, circulates nutrients and removes waste.2.1.1 BrainGrey matter is mostly found at the edge of the brain, the cortex, while white matterforms tree-like structures in the interior (Figure 2.3). These white matter tracts con-45Figure 2.3: T1-weighted MRI of the brain, showing the white matter and grey mat-ter anatomy.nect different regions of grey matter in the cortex to facilitate cognition, motor control,sensory processing and autonomic functions. Brain white matter regions of interestinvestigated with qMRI in this thesis include anterior, superior and posterior corona ra-diata (ACR, SCR, PCR), the anterior, retrolenticular and posterior limb of the internalcapsule (AIC, RIC, PIC), the external capsule (EXC), the genu, body and splenium ofthe corpus callosum (GEN, BOD, SPL), the cerebral peduncles (CEP), the cingulum(CIN) and the hippocampal cingulum (CIH), the saggital stratum (SAS), and the su-perior fronto-occipital and longitudinal fasciculi (SFF, SLF). Three deep grey matterregions, the caudate (CAU), putamen (PUT) and thalamus (THA) were also investigated(Figure 2.4).46Figure 2.4: White matter and grey matter brain regions of interest used in this thesis, from the JHU White Matter Labelsand MNI structural atlas respectively. [6–8]472.1.2 Spinal cordFigure 2.5: Segments ofthe human spinalcord.The spinal cord begins at the brainstem and connects thebrain to the peripheral nervous system, transmitting signalsfrom the motor cortex to the body, and from the body backto the sensory cortex. It also controls reflexes, producingmotor responses to stimuli through reflex arcs without braininvolvement. In the spinal cord, the white matter surroundsthe grey matter “butterfly” (Figure 2.6). The spinal cord is40 - 45 cm long and made up of 31 segments, each with apair of spinal nerves. It is protected by CSF, three layers ofmeninges, the vertebrae, and intervertebral disks made offibrocartilage. The spinal cord segments are classified intoeight cervical (C1-C8), twelve thoracic (T1-12), five lum-bar (L1-L5), five sacral (S1-S5) and one coccygeal. Eachpair of spinal nerves attaches to the associated segment viatwo roots, one bringing sensory information to the dorsalhorn of the grey matter (except C1, which does not have adorsal nerve root), and one taking motor information fromthe ventral horn of the grey matter to the periphery.In Chapter 5 of this thesis, spinal cord white matter wasdivided into five areas: the cuneate and gracile fasciculi,lateral corticospinal tracts, and the lateral and ventral funi-culi. The grey matter was split into the dorsal and ventralhorns (Figure 2.7).48Figure 2.6: T2-weighted MRI of the spinal cord cross-section, showing the whitematter and grey matter “butterfly” shape.Figure 2.7: Spinal cord regions of interest used in this thesis, from the histologicalatlas made by Sengul et al. [9].492.2 Myelin structureMyelin is a lipid-rich plasma membrane extension synthesized by oligodendrocytes inthe central nervous system. (In the peripheral nervous system myelin is produced bySchwann cells.) The sections of myelin in between the nodes of Ranvier are calledinternodes. Each oligodendrocyte can myelinate 30 or more internodes. [116] Myelinis composed of phospholipid bilayers embedded with proteins and interspersed withfluid layers. [117] Figure 2.8 shows the complex structure of the sheath. Myelin isa dielectric insulator with an unusually high lipid content, comprising between 71−83% of the dry weight. [116] The alternating lipid bilayers are the plasma membraneof the oligodendrocyte. When compact myelin is formed, the cytoplasm within theoligodendrocyte process is eliminated, and the two sides of the cell membrane come intocontact forming the major dense line. Between the external surfaces of the membranesis extra-cellular fluid (the intra-period line).2.3 Myelin developmentWhile all major white matter tracts in the brain are present by the end of gestation, [118]myelination of these tracts is a process which continues throughout infancy, childhoodand adolescence. [119] This myelination allows for more rapid and efficient neuralcommunication and has been linked to many aspects of cognitive development. [120–123] While this myelination is occurring, the volume of white matter in the brain alsoincreases and changes to axon properties can be measured with DTI. [124–126]2.3.1 qMRI to measure myelin developmentFA is found to rapidly increase during childhood and adolescence while MD decreases.[127–130] FA, MD, AD and RD have also been linked to a range of cognitive processes,including math [131] and reading ability. [126] Although DTI is sensitive to a range of50Figure 2.8: The structure and composition of myelin. Reproduced with permissionfrom Laule et al. [10].white matter tissue changes, it is not specific to myelin. Diffusion anisotropy is presentin white matter tissue in the absence of myelin and DTI metrics reflect many aspectsof white matter microstructure, including axon density, diameter, alignment and myeli-nation, many of which change during brain maturation. [132, 133] Rapid myelinationhas been demonstrated in early childhood and adolescence using mcDESPOT-derivedMWF. [90, 134–138]2.4 DemyelinationThere are many diseases which result in damage to or loss of myelin. The most commonis MS, which is an inflammatory demyelinating disease of the central nervous system. Inthe brain and spinal cord of a person with MS, immune cells attack myelin, producing51distinct areas of demyelinated white matter called lesions. This can cause a range ofphysical and cognitive symptoms. Other less common causes of central nervous systemdemyelination include autoimmune diseases such as neuromyelitis optica, transversemyelitis and acute disseminated encephalomyelitis, as well as inherited demyelinatingdiseases such as leukodystrophies.2.5 Spinal cord injuryMyelin loss can also be caused by injuries to the central nervous system tissue, suchas a stroke, concussion or SCI. After a traumatic SCI, mechanical trauma to the cordtissue results in a primary injury and axon transection. Build up of edema, and micro-haemorrhages leading to ischemia begin immediately after the injury. [139] There thenfollows an extended period of secondary injury and tissue degeneration due to processessuch as neurogenic shock, excitotoxicity, edema and inflammation. Oligodendrocytesbegin to undergo both necrotic and apoptotic cell death immediately, [140] resulting inslow myelin loss over years. [141] This secondary cascade causes progressive harm tothe spinal cord and has a substantial effect on long-term outcome and recovery.The timeline and pathophysiology of secondary injury remain incompletely under-stood; it is, however, acknowledged that inflammation, myelin loss and axonal destruc-tion are important responses within the injured spinal cord. Traumatic SCI initiates arobust immune response, with activated microglia observed in the human spinal cordwithin a day after injury, and macrophage infiltration occurring at ∼ 2− 5 days post-injury. [142] These inflammatory cells ingest myelin debris and exacerbate myelin loss.[143] The presence of myelin debris may also invoke a greater inflammatory response,causing a cyclical process of continual damage. [144] In the context of traumatic injury,inflammation can be considered a loose proxy for tissue damage. In contrast to MS-associated demyelination, where inflammation drives the damage and may vary between52Figure 2.9: The process of myelin loss after traumatic SCI. Reproduced with per-mission from Textbook of Neural Repair and Rehabilitation [11].patients and disease stages, in traumatic SCI inflammation is considered “reactive”, a re-sponse to the damage within the tissue. In other words, the greater the inflammation, thegreater the expected tissue damage at the microstructural level, including damage toaxons and myelin sheaths.2.5.1 Wallerian degenerationWallerian degeneration begins after the primary injury and continues for years, com-prising disintegration of the distal part of the axons and associated myelin sheaths af-ter connection with the neuronal cell body is severed (Figure 2.10). [145, 146] Theaxon swells due to accumulation of organelles and eventually disintegrates, leaving themyelin sheath which then breaks down over a long period. Wallerian degeneration oc-curs in both the central and peripheral nervous system but is much slower in the central53Figure 2.10: The processes of retrograde and Wallerian degeneration after trau-matic SCI. Reproduced with permission from Textbook of Neural Repairand Rehabilitation [11].nervous system which is thought to be a factor in the lack of new axon growth in thecentral nervous system after an injury. This asymmetric axon loss can be seen caudal tothe lesion in the motor pathways and rostral to the lesion in the sensory tracts. [147]2.5.2 ISNCSCI ScaleThe severity of a SCI can be classified according to the International Standards for Neu-rological Classification of Spinal Cord Injury (ISNCSCI) scale [148] which grades theremaining motor and sensory function below the injury. In a complete injury (ASIAImpairment Scale (AIS) A), there is no motor or sensory function below the injury levelwhereas in an incomplete injury (AIS B,C,D) some motor and/or sensory function ispreserved.54Spinal cord injuries are heterogeneous in severity, neurological level and injurymechanism. Clinical trials are increasingly focussed on providing targeted therapiesfor SCI patient sub-populations, stratified by specific injury metrics. This relies on ac-curate information about the complex injury microstructure and how it is changing overtime. [149]Conventional MRI is an essential component of the clinical assessment of traumaticSCI in the acute and subacute phases, and signal changes observed on these imagescan correlate with injury severity and predict outcome. Abnormalities on T1- and T2-weighted clinical MRIs are associated with a wide variety of pathological changes in-cluding axonal loss, activated microglia, gliosis, reactive astrocytes, vacuolation, edema,and blood spinal cord barrier disruption. Therefore, T1 and T2-weighted signal changeswithin the spinal cord are not pathologically specific, and it is difficult to know what ex-actly is occurring histologically to produce the hyper- and hypo-intensities observed onMRI. MRI biomarkers specific to myelin loss and inflammation in traumatic SCI wouldenable a more detailed assessment of secondary damage, better stratify injury severity,and more accurately predict neurologic recovery. Such biomarkers might also be usedto track the progression of injury or the response to novel interventions.2.5.3 qMRI for spinal cord injuryT2-weighted MRI and DTI has detected Wallerian degeneration with T2 signal changesoccurring 1-3 months post-injury [150] and DTI abnormalities reported in the chronicstage. [151] Several other candidate qMRI techniques may be useful for measuringtissue microstructure and differentiating injury processes.MWI has been successfully applied to measure spinal cord myelin in vivo, [42] trackmyelin changes over time in MS, [38] and is found to correlate with measures of myelinfunction by electrophysiology in cervical spondylotic myelopathy. [152] The MWF has55been found to include signal from myelin debris in an animal model of traumatic SCI.[153]In vivo spinal cord ihMT has recently been demonstrated, [154, 155] and althoughapplications to patient populations are still emerging, early findings appear promis-ing. For example, in both Amyotrophic Lateral Sclerosis (ALS) and MS, reductionsin ihMTR were larger, more widespread, and more correlated with clinical scores thanwere DTI metrics. [71, 74]Histological validation has provided some evidence supporting the interpretation ofdiffusion metrics in MS spinal cord ex vivo, revealing a link between higher RD andlower neurite dispersion in demyelinated lesions. [156, 157] FA is decreased in bothacute and chronic SCI, and correlates with motor scores, [151, 158, 159] and somatosen-sory evoked potentials. [160] MD and AD are decreased in acute SCI, [159, 161] butincreased in chronic SCI. [162] In the presence of damage, modelling and interpreta-tion of DTI measures can be challenging. If an MRI voxel contains fibres oriented inmultiple directions, this will reduce the FA. [102] Crossing fibres are less of an issue inhealthy spinal cord where fibres are mostly aligned, but in areas of injury FA may beconfounded by fibre misalignment.2.6 Thesis overviewThe main body of this thesis is formed of three chapters. Chapter 3 describes the appli-cation of MWI, ihMT, MT and DTI to the study of healthy adult and pediatric brains.This is divided into Section 3.1 which outlines the reproducibility, correlation and whitematter myelination rankings for MTR, ihMTR, and MWF measured in 17 healthy adultsacross the regions of interest in Figure 2.4. Section 3.2 details the construction of aMWF and DTI atlas for children aged 9-10 years. This atlas is used to explore correla-tions between adult and pediatric myelination across white matter.56Chapter 4 concerns the effect of white matter fibre orientation on qMRI metrics.Section 4.1 presents the analysis of the orientation dependence of MTR, ihMTR, andMWF using the same data as in Section 3.1. The orientation dependence curves areseparated by region of interest and pronounced differences are found in the trends withfibre angle between different white matter tracts. Section 4.2 explores possible causes ofthe fibre orientation dependence observed in ihMTR. An aligned phospholipid bilayersample was rotated in an Nuclear Magnetic Resonance (NMR) spectrometer to measurethe orientation dependence of M2 and T1D in this planar system. The results were thenused in a cylindrical model for a myelinated axon to compare the modelled orientationdependence to that seen in vivo.Chapter 5 reports the results of the application of MWI, ihMT, DTI, DKI and DBSIto study human traumatic SCI tissue from the International Spinal Cord Injury Biobank(ISCIB). Section 5.2 characterises the sensitivity and specificity of ihMTR, MWF, DTIand DKI metrics to myelin lipids and inflammatory cells in human traumatic SCI tissueex vivo. Carefully matched qMRI slices and histological staining data are correlatedin six patients from the ISCIB across the regions of interest in Figure 2.7. Section 5.3discusses the application of these qMRI techniques to measure changes to the tissuemicrostructure along the cord. We found evidence for Wallerian degeneration in thecords with injury-death interval longer than two months.Each chapter contains the pertinent methods and ends with a Future Research sectiongiving some next steps suggested by the results of this thesis. Chapter 6 provides a briefconclusion.57Chapter 3Myelin imaging in the healthy brainThe ability to quantify myelin in vivo is of huge importance, especially as remyelinationtherapies for MS start to reach clinical trials. Accurate myelin measurements can giveinsight into childhood development, healthy aging and the natural history of the widerange of diseases which impact myelination. Many myelin-sensitive contrast mecha-nisms have been developed to provide qMRI myelin metrics.There are many steps required before a myelin-sensitive qMRI metric can be usedclinically:1. Technical development of the acquisition and analysis of the technique is required,achieving goals such as optimizing SNR and reducing scan time.2. Validation of the technique’s myelin sensitivity must be performed, comparing theqMRI data to histological staining for myelin in a variety of contexts, so that re-searchers can be confident that changes in the metric pertain to changes in myelin.3. Normative values in healthy individuals must be collected, which can be used todetermine the measurement’s dynamic range and reproducibility, and compare topathology.584. The technique can be applied to pathologies of interest, to determine how muchthe metric changes in each case.In this chapter, I report results relating to the third step of the process described above,in both the healthy adult and pediatric brain.593.1 Myelin biomarkers in the healthy adult brainOur aim was to investigate the utility of three myelin-sensitive methods in the healthyadult brain in vivo: MT, ihMT and MWI. To this end, we compared the metrics, assess-ing their agreement on myelination rankings between white and grey matter regions,correlation between metrics and scan-rescan variability.3.1.1 MethodsDemographicsSeventeen healthy volunteers participated in this study: sex = 5/12 male/female, meanage = 26 yrs, range = 21-48 yrs. Prior written consent was obtained in accordance withthe local clinical research ethics board.MRIParticipants were scanned at 3 Tesla (Philips Achieva) with an 8-channel receive-onlyhead-coil to obtain:1. 3D T1-weighted scan (3DT1): MPRAGE, TR/TE/T I = 3000/3.6/984 ms, resolu-tion = 1× 1× 2 mm3, Field of View (FoV) = 256× 256× 160 mm3, 3 mins 11secs.2. MT/ihMT: GE, TR/TE = 130/3.5 ms, flip angle = 20◦, resolution = 1×2×5 mm3,40 slices, FoV = 230×190×100 mm3, prepulse: twelve 0.9 ms Hanning pulseswith interpulse intervals of 1.5 ms, frequency offset±7kHz, 10 mins 35 secs. Thisgave a medium T1D-filtering effect.3. MWI: 48 echo 3D GRASE, TR/∆TE = 1073/8 ms, effective TR = 689 ms, resolu-tion = 1×2×5 mm3, 40 slices, FoV = 230×190×100 mm3, 7 mins 30 secs.60Three participants were scanned twice, in separate scan sessions a few weeks apart (Par-ticipants 1, 2 and 3 in Figure 3.5). Three different participants were scanned twice in thesame imaging session with no readjustment between scan and rescan (Participants 4, 5and 6 in Figure 3.5).Data processingGaussian filtering was applied to the five raw ihMT image datasets to minimize trun-cation artifacts. ihMT images were linearly co-registered and light bias regularizationwas applied (0.1%) using SPM12 realign and biasreg. [163] The scans were combinedto create voxelwise MTR (Equation 1.20) and ihMTR (Equation 1.21) maps. Note thatthe MT saturation frequency offset is higher than that typically used in MT studies, 7kHz compared to 1-2 kHz for standard MT, resulting in lower MTRs. For the MWIdata, the T2 decay curve from each voxel was analyzed using an NNLS algorithm withflip angle optimization and stimulated echo correction, a process described in Prasloskiet al. [164]. The MWF was defined as the fraction of the signal from each voxel withT2 < 40 ms. [30]Regions of interestAnalysis followed a standard procedure: the first echo scan from GRASE and the S0scan from ihMT were brain extracted using FSL BET [165] and linearly registered toeach participant’s 3DT1 scan using FSL FLIRT and 6 degrees of freedom. 3DT1 scanswere registered to MNI standard space using FSL FNIRT. [166] The warp producedwas used to register regions from the JHU White Matter Labels atlas [6] and the MNIstructural atlas [7, 8] to the 3DT1 space.All regions in both atlases were included except those which were outside of the FoVor had fewer than 400 voxels after registration. Cortical grey matter regions were also61excluded as the 5 mm slice thickness used to acquire the MT, ihMT and MWI metricswould result in a significant partial-volume effect in these thin regions. Left and rightbilateral regions were combined, resulting in 17 white matter and 3 deep grey matterregions for investigation (Table 3.1). White matter and grey matter masks were createdfor each participant with FSL FAST, which gives a map of the probability that eachvoxel is each tissue type. [167] The maps were thresholded at 90% probability for whitematter and 30% probability for grey matter (as FAST segmentation often underestimatesthe size of deep grey matter regions), and used to mask the data to include only whiteor grey matter voxels for the relevant regions of interest. ihMTR, MTR and MWF meanand standard deviation across participant region means were extracted.Atlas creationThe metric maps, which were registered to the MNI standard space, were averaged.Voxels which were outside of the brain for one or more of the seventeen participantswere masked out. Metric maps for ihMTR, MTR, MWF and fibre angle averaged acrossall 17 healthy adult participants are provided for other researchers to use at: https://sourceforge.net/projects/ihmtr-mwf-wm-angle-atlas/Statistical analysisCorrelation The Shapiro-Wilk test for normality was performed on the ihMTR, MTRand MWF region values. Based on a p-value threshold P < 0.05, all three metrics werefound to be non-normal when including both white and grey matter regions. MTR wasthe only normally distributed metric across white matter voxels while all three metricswere normally distributed across grey matter voxels.There are two coefficients commonly used to evaluate the correlation between twometrics: the Pearson coefficient, which quantifies the linear relationship between two62continuous variables and the Spearman coefficient which measures the monotonic rela-tionship between two variables, based on the ranks of the data points rather than theirvalues. Both vary between -1.0 for a perfect negative correlation and +1.0 for a perfectpositive correlation, with 0.0 representing a completely uncorrelated pair of variables.If one of the variables is ordinal and not continuous, only the Spearman coefficient canbe used. The Spearman coefficient is also robust to outliers, as it only uses the ranksof the data points, and so data with outliers influencing the linear correlation should beanalysed with the Spearman coefficient. Otherwise, the choice of coefficient depends onthe expected relationship between the two variables. If a linear correlation is expected,Pearson is most relevant, if a monotonic non-linear relationship is of interest, Spearmanshould be used. Often it is useful to calculate both coefficients, as the difference betweenthem can provide information on the correlation. For example, if the magnitude of thePearson coefficient is lower than the Spearman coefficient, this indicates that the rela-tionship between the two variables is monotonic but non-linear. Neither metric assumesa normal distribution of the variables.The Pearson correlation coefficient (r) and Spearman rank correlation coefficient (ρ)along with confidence intervals and p-value for non-correlation were calculated to testthe bivariate correlation between ihMTR and MTR, ihMTR and MWF and MTR andMWF.Reproducibility MTR, ihMTR and MWF from the scan-rescan data sets were plottedon a Bland-Altman graph. Scan-rescan Coefficient of Variation (CoV) was calculated63for each region of interest asCoVscan-rescan =stdscan-rescandynamic range×100 (3.1)=|scan− rescan|/√2dynamic range×100 (3.2)The CoV is usually normalised to the mean, however in this case we chose to normaliseto the dynamic range (i.e. the range of each metric measured in regions of interestacross healthy white and grey matter) as it is the relevant parameter to compare thestandard deviation to for the purposes of determining each metric’s ability to detectmyelin differences.An inter-subject CoV was also calculated, usingCoVinter-subject =stdROIdynamic range×100 (3.3)CoV in both cases was then multiplied by (1+1/4n) where n is the number of measure-ments (2 for the scan-rescan, 17 for the inter-subject calculation). [168]3.1.2 ResultsFive example data sets are shown in Figure 3.1. MTR had the least white/grey mattercontrast, while MWF had the most. Visual inspection suggests a greater variation inMWF across white matter than either of the MT-based techniques. ihMTR and MWFare both bright in the internal capsules and corpus callosum. A faint ringing artifact isvisible at the edge of the brain in the MTR and ihMTR data. A full summary of themetric values for each region of interest is given in Table 3.2.The ranking of white matter brain regions by MWF was more similar to ihMTRthan MTR (Figure 3.2). MWF had a larger dynamic range (2-28%) than either ihMTR64Figure 3.1: Example MTR, ihMTR and MWF maps from five participants.(1-5%) or MTR (14-23%). MWF also had the highest variability between differentpeople within each white matter region (standard deviation = 1.8%) compared to ihMTR(standard deviation = 0.3%) and MTR (standard deviation = 0.7%), when normalised bythe dynamic range however, these values become similar: 7.8% for MTR, 7.4% forihMTR, and 6.9% for MWF.We found a strong correlation between ihMTR and MTR (Pearson r = 0.70, r con-fidence interval (ci) = 0.64-0.75, P < 0.001) and between ihMTR and MWF (Pearson r= 0.79, ci = 0.74-0.82, P < 0.001) and a weaker correlation between MTR and MWF(Pearson r = 0.54, ci = 0.46-0.61, P < 0.001). (Figure 3.3). When analysing the white65Table 3.1: List of the region of interest abbreviations used.Abbreviation RegionACR Anterior corona radiataAIC Anterior limb of the internal capsuleBOD Body of the corpus callosumCEP Cerebral peduncleCIH Cingulum (hippo-campus)CIN Cingulum (cingulate gyrus)EXC External capsuleGEN Genu of the corpus callosumPCR Posterior corona radiatPIC Posterior limb of the internal capsulePTR Posterior thalamic radiationRIC Retrolenticular part of the internal capsuleSAS Sagittal stratumSCR Superior corona radiataSFF Superior fronto-occipital fasciculusSLF Superior longitudinal fasciculusSPL Splenium of the corpus callosumWM ROI WM ROI averageWM ALL All WM voxels chosen by FASTCAU CaudatePUT PutamenTHA ThalamusGM ROI GM ROI averageGM ALL All GM voxels chosen by FASTmatter regions separately, the correlation between ihMTR and MWF was still strong(Pearson r = 0.66, P < 0.001), while the correlations between ihMTR and MTR andMTR and MWF were much weaker, although still significant (WM: ihMTR vs MTR:Pearson r = 0.21, P < 0.001; MTR vs MWF: Pearson r = 0.17, P = 0.004). In the greymatter there was still a significant correlation between ihMTR and MWF (Pearson r =0.41, P= 0.02), but both correlations with MTR were not significant. We also performedSpearman rank correlations and compared these to the Pearson correlations. There wereno large differences between Pearson r and Spearman ρ for all of the correlations inves-66Table 3.2: The inter-subject and scan-rescan region of interest data. Means andstandard deviations from all 17 participants are given in ihMTR/MTR/MWFpercent units. CoVs are given as percentages of the dynamic range of eachmeasurement, which was 8.8% for MTR, 4.1% for ihMTR, 25.5% for MWF.WM/GM ROI gives the average metric value over all white matter/grey matterRegion Of Interests (ROIs). WM/GM ALL gives the average metric value overall WM/GM voxels as segmented by FAST (which is a larger volume than thecombined ROIs).MTR ihMTR MWFAbbre-viation inter-subjmeaninter-subjstdscan-rescanCoVinter-subjmeaninter-subjstdscan-rescanCoVinter-subjmeaninter-subjstdscan-rescanCoVACR 20.6 0.6 5.0 3.7 0.2 4.4 7.7 1.4 2.2AIC 20.4 0.7 8.2 3.5 0.4 7.5 11.3 1.6 2.4BOD 20.4 0.8 4.7 3.7 0.3 6.5 9.6 1.7 3.1CEP 20.4 0.6 7.1 4.4 0.5 10.5 22.2 3.1 3.2CIH 19.6 0.9 8.7 2.9 0.4 8.3 8.7 2.1 4.3CIN 19.8 0.6 6.1 2.9 0.3 6.3 6.1 1.4 2.3EXC 20.1 0.6 5.8 3.8 0.3 4.1 6.6 1.1 2.8GEN 21.6 0.8 9.4 3.6 0.3 5.4 9.2 1.5 2.0PCR 19.5 0.7 4.3 3.9 0.3 3.9 10.6 1.6 2.2PIC 20.7 0.6 7.1 4.8 0.3 8.5 16.2 1.8 3.3PTR 20.8 0.7 5.2 3.9 0.2 4.4 13.4 2.1 4.0RIC 20.8 0.9 6.6 4.0 0.2 5.9 12.6 1.9 2.0SAS 21.6 0.7 7.0 3.7 0.3 4.4 10.4 2.0 3.2SCR 20.0 0.6 5.5 4.2 0.3 4.1 12.1 1.7 2.9SFF 20.6 0.6 5.7 3.7 0.4 9.4 9.9 1.8 2.4SLF 20.4 0.5 3.9 3.7 0.2 4.5 11.5 1.5 2.1SPL 21.5 0.7 6.2 4.0 0.3 6.4 13.2 1.8 2.9WM ROI 20.5 0.7 6.3 3.8 0.3 6.1 11.3 1.8 2.8WM ALL 19.1 0.5 3.6 3.2 0.2 3.0 9.0 1.2 2.4CAU 15.8 0.7 7.6 1.7 0.3 6.5 3.0 0.6 0.9PUT 17.1 0.6 7.5 2.1 0.3 4.8 3.7 0.9 1.2THA 18.5 0.6 7.7 2.3 0.4 9.3 3.5 0.8 2.8GM ROI 17.1 0.6 7.6 2.1 0.3 6.8 3.4 0.8 1.6GM ALL 15.4 0.5 5.6 1.6 0.2 2.3 5.0 0.7 0.967Figure 3.2: The rankings of white and grey matter regions by MTR, ihMTR andMWF. Each point represents the mean for each region over all 17 participantswith error bars giving the standard deviation. At each end, wide, shaded linesshow the dynamic range across white and grey matter and bars give the meanand scan-rescan CoV in white matter for each myelin metric. See Table 3.1for ROI abbreviations.tigated, implying that the data is fairly uniformly distributed, and outliers are not greatlyinflating the Pearson r values. Table 3.3 gives all of the correlation analysis parameters.Bland-Altman analysis (Figure 3.5a) showed that MTR had a mean scan-rescan dif-ference fairly close to zero (-0.3%). There was no bias in the differences with increasingMTR. Average scan-rescan CoV for MTR, given as a percentage of the dynamic range,was 3.6% over all white matter and 6.3% on average in a ROI.For ihMTR (Figure 3.5b) the mean scan-rescan difference was again close to zero68Table 3.3: Summary of the correlation statistics for the Pearson and Spearman cor-relations between MTR, ihMTR and MWF. Pearson linear correlations andstatistics are correlations shown in Figure 3.3.ihMTRvs MTRihMTRvs MWFMTRvs MWFAll ROIsPearsonr 0.70(0.64-0.75)0.79(0.74-0.82)0.54(0.46-0.61)p < 0.001 < 0.001 < 0.001slope 0.37 0.14 0.18intercept -3.78 2.17 18.22Spearmanρ 0.48(0.39-0.56)0.79(0.75-0.83)0.51(0.43-0.59)p < 0.001 < 0.001 < 0.001WM ROIsPearsonr 0.21(0.10-0.32)0.66(0.59-0.72)0.17(0.05-0.28)p < 0.001 < 0.001 0.004slope 0.13 0.09 0.04intercept 1.21 2.81 20.10Spearmanρ 0.16(0.05-0.27)0.67(0.60-0.73)0.22(0.10-0.32)p 0.005 < 0.001 < 0.001GM ROIsPearsonr 0.24(-0.11-0.54)0.41(0.08-0.66)-0.04(-0.38-0.30)p 0.17 0.02 0.81slope - 0.17 -intercept - 1.60 -Spearmanρ 0.18(-0.16-0.49)0.44(0.13-0.68)0.04(-0.30-0.38)p 0.30 0.008 0.8169Figure 3.3: Pearson correlations between MTR, ihMTR and MWF. Each pointrepresents the ROI average from one participant, error bars give standarddeviations within each ROI. Solid lines show significant correlation acrossall ROIs, dotted lines show significant correlations within WM or GM ROIsalone. See Table 3.1 for abbreviations.(0.2%) and there was no bias in the differences with increasing ihMTR. Average scan-rescan CoV for ihMTR, as a percentage of the dynamic range, was 3.0% over all whitematter and 6.2% on average in a ROI.For MWF (Figure 3.5c) the mean scan-rescan difference was close to zero (-0.3%)and there was no bias in the differences with increasing MWF. Average scan-rescanCoV for MWF, as a percentage of the dynamic range, was 2.4% over all white matterand 2.8% in on average in a ROI.70(a) ihMTR vs MTR(b) ihMTR vs MWF71(c) MTR vs MWFFigure 3.4: Pearson correlations between MTR, ihMTR and MWF separated byregion. Each point represents the ROI average from one participant, errorbarsgive standard deviations within each ROI.3.1.3 DiscussionThree myelin sensitive MRI methods were compared in terms of their myelination rank-ings for brain ROIs, correlation with each other and scan-rescan reproducibility. Eachmethod has strengths and limitations which may dictate their suitability for differentresearch and clinical questions.RankingsThere was some agreement between ihMTR and MWF in the white matter myelinationrankings. In particular, the posterior limb of the internal capsules and the cerebral pe-duncles were the two areas with highest myelin as measured by both ihMTR and MWF.72(a) MTR(b) ihMTR73(c) MWF. Six participants had ihMTR and MTR scan-rescan data collected, but only four ofthese had a second MWF data set collected, hence there are only 4 participants on in this figure.Figure 3.5: Bland-Altman graphs analysing the reproducibility of MTR, ihMTRand MWF in healthy adults. Each point represents a ROI from a participant.Colours represent each ROI, see key in Figure 3.3. Filled shapes representwhite matter ROI, empty shapes represent deep grey matter ROI. Grey linesrepresent mean scan-rescan difference and the limits of agreement (definedas ±1.96 times the standard deviation of the differences). See Table 3.1 forROI abbreviation definitions and Figure 3.3 for ROI colours.The cerebral peduncles (which is made up of corticobulbar, corticopontine, and corti-cospinal fibres) have previously been reported as having high MWF, attributed to thepresence of highly myelinated organised fibres, reaching MWF values almost as high asin the spinal cord. [169] The posterior limb of the internal capsule (which also containsthe corticospinal tract) is known to have a unique T2 distribution in which the intra/ex-tracellular water peak is unusually broad. The short T2 end of this peak can sometimescontribute to a high MWF. [170] We looked at the T2 distribution of the cerebral pedun-74cles and found that the 40 ms cut-off accurately separated the myelin water peak fromthe intra/extracellular water peak. However, the myelin water T2 of the cerebral pedun-cles was indeed longer than it is in most white matter ROIs. Our interpretation is that thecerebral peduncles are similar to spinal cord tissue in that they have thicker myelin, per-haps due to the speed with which the axons need to carry action potentials over a largedistance, and that this increases the MWF. Supporting this argument, ihMTR, which isnot determined from the T2 distribution, was also found to be high in the cerebral pe-duncles, suggesting that the high MWF is primarily due to high myelin content there.Using a combination of MWI and ihMT techniques, which have fundamentally differentcontrast mechanisms, in this way can increase confidence in statements about relativemyelination because confounding factors are less likely to affect both metrics in thesame way. There was no consistent agreement between the MWF and MTR rankings.CorrelationThe moderately strong correlation between ihMTR and MWF across white matter wassimilar to that found in a smaller study by Ercan et al. [64] and suggests that these twotechniques are detecting similar variations in myelination. The correlations are veryclearly bimodal, and correlations were also fit to the grey and white matter separately.Correlations were performed for each ROI separately (Figure 3.4) which resultedin only one significant positive correlation between ihMTR and MTR, in the superiorlongitudinal fasciculus, and none between MTR and MWF. The correlation betweenihMTR and MWF, however, remained strong and significant in 12 of the 17 white mattertracts and one grey matter tract. There are many possible explanations for why there wasno correlation between ihMTR and MWI in 5 of the white matter regions. The thicknessof the myelin sheath and the size of axon radii have been shown to affect the rate ofexchange between water compartments. [53, 171] Perhaps, in areas with thinner myelin75and smaller axons, such as the genu of the corpus callosum, the MWF is decreasedcompared to ihMTR due to exchange. Alternatively, orientation dependence effectscould be affecting each metric differently (see Chapter 4). Further research is neededto investigate the source of differences in myelin measurements between techniques ineach ROI.A question which has yet to be adequately addressed in the ihMT literature is whetherihMTR can be measured in plasma membranes and other non-myelin lipids in brain greymatter. T1D, fit with a single exponential, is significantly reduced in grey matter com-pared to white matter. [67, 70] This could be due to signal from lipids in ROIs other thanmyelin, which have lower T1D due to their molecular mobility or chemical environment,contributing to the ihMTR. In our results, the correlation between ihMTR and MWF isonly very weakly seen in grey matter; perhaps signal from non-myelin lipids is reducingthe ihMTR/MWF correlation. However, this could also be due to the fact that the lowihMTR and MWF values in grey matter are close to the noise floor, resulting in valueswhich, while somewhat reproducible, may have little biological relevance.ReproducibilityTo compare the CoVs we found to reproducibility studies in the literature, we had tonormalise theCoV to the mean, instead of the dynamic range normalisation we had used.We found that average scan-rescan CoV of MTR in our study was slightly higher thanpreviously reported. [172, 173] Average scan-rescan CoV of ihMTR over all whitematter was 5% of the mean, and in an ROI was 6.9% of the mean, which is similar tothat found by Ercan et al. [64] though significantly higher than that found by Zhanget al. [63] and Mchinda et al. [86]. Average scan-rescan CoV of MWF over all whitematter was 7.4% of the mean, higher than the 4% previously reported. [36] This may bedue to the GRASE acquisition used in this study, which has a higher bandwidth readout76and therefore somewhat lower SNR. The GRASE sequence is also more demanding forthe gradient hardware and thus potentially more sensitive to instabilities than the 7-slice3D MWI measurements made by Meyers et al. [36]. Future studies will investigate thescan-rescan CoV with an updated MWI method which employs compressed sensing foracceleration. [25]There are two components which combine to influence the standard deviation ofan MRI metric: reproducibility, influenced by the measurement error, and natural bi-ological variation. Average scan-rescan CoV, for white matter ROIs, was 0.6% MTR,0.3% ihMTR and 0.7% MWF, across dynamic ranges of 14.4−23.2% MTR, 1.2−5.4%ihMTR and 2.0− 27.5% MWF. MWF had a large dynamic range and a high CoV be-tween people (resulting in large standard deviation error bars in Figure 3.2); the between-subject CoV is 2.5 times the scan-rescan CoV, which implies that MWF is detecting truevariations between individuals. Despite the more complicated analysis required by theMWF measurements to extract T2 distributions, when normalized by the measurementdynamic range, MWF has the smallest scan-rescan CoV of the three measures. ihMTRhad a smaller dynamic range and lower biological variability, with the between-subjectCoV only 1.3 times the scan-rescan CoV. MTR gives very reproducible values but onlyin a very narrow range, at least in healthy tissue, reducing the information availablewhen scanning healthy participants.LimitationsThis study expands on a previous work by Ercan et al. [64] by including twice as manyparticipants and extending the analysis beyond white matter to include grey matter re-gions. Our study had some limitations; firstly, the ihMT sequence used did not includerecent developments to account for T1 and B1 inhomogeneities. [90] We also did notexperiment with changing the T1D-filtering to see how this would influence grey and77white matter ihMTR values, [66] which is an interesting direction for future research.The linear correlation analysis was performed using each subject’s ROI mean valuesan individual data points. This introduces a possible bias to the correlation coefficientsand p-values as the ROI values for each subject are not independent. Future analysisshould consider using a multi-parameter mixed regression model to obtain the correla-tion coefficients. However, as this analysis was mainly aiming to compare the corre-lation strengths between techniques, we believe that the results found using our simpleapproach are still helpful. Finally, the MTR saturation was performed at a large offsetfrom water resonance (7 kHz vs 1−2 kHz) because we were using data acquired fromthe ihMT sequence, which resulted in lower than typical MTRs. This may mean thatsome of the MTR results and conclusions are different to what would be found usinga typical MTR sequence. In particular, the difference between white and grey matterMTR was smaller than in previous studies using a smaller pre-pulse offset [174]. Theeffect of pre-pulse offset on the MTR values of the short and long-T2 water pools wasmeasured by Vavasour et al. [175]; they found that the difference in MTR between thesetwo water pools decreased with increasing pre-pulse offset.3.1.4 ConclusionThe moderately strong correlation and similarity in myelination rankings between ihMTRand MWF shows that these two techniques are detecting the same variations in whitematter myelination through different contrast mechanisms. Average scan-rescan CoVfor white matter ROIs was 0.6% MTR, 0.3% ihMTR and 0.7% MWF, across dynamicranges of 14.4−23.2% MTR, 1.2−5.4% ihMTR and 2.0−27.5% MWF. While ihMTRhas a myelin sensitivity similar to MWF, the results from this study suggest that MWFexhibits a greater dynamic range and thus, with recent improvements in the acquisition,may provide more power to address research questions about myelination.783.2 Myelin biomarkers in the healthy pediatric brainQuantifying pediatric brain myelination is important in order to understand both healthydevelopment and the many pediatric brain diseases which affect white matter microstruc-ture. Narrowly age-specific qMRI control data is needed when investigating pediatricbrain pathologies due to the rapid alterations occurring throughout the brain during de-velopment. An age-specific structural template is also necessary, as registration of pe-diatric brain data to an adult standard space can cause increased deformation and bias.[176, 177] Recruiting age-matched pediatric controls can be challenging and for thisreason it is important that age-specific pediatric structural templates and qMRI atlasesare available to researchers. Atlases of DTI metric distributions in healthy adult [178–181] and of pediatric brains for some age groups are available. [130, 182, 183] Adultmulti-echo-T2 based MWF atlases in both brain and cervical spinal cord have recentlybeen published, [184–186] but, to our knowledge, no such atlases exist for children.The goal of our study was to create pediatric diffusion and MWF atlases for healthychildren, along with a corresponding structural template, to use as normative data forregistration and reference in pediatric brain research. As an initial study we chose ahomogeneous cohort in the age range of 9− 10 years old. This age group is of inter-est as it has never been studied with MWI before and is a period of rapid learning andcognitive maturation before adolescence. The structural, DTI and MWF atlases wereproduced using Advanced Normalisation Tools (ANTs) [187] software which outper-forms cross-sectional template analysis methods, especially with pediatric brain data,[188–190] along with FSL affine registration. [191] ANTs transformations allow forlarge deformations, maximising anatomical alignment while preserving natural varia-tions within a population. Our DTI and MWI atlases can be used to produce Z-scoremaps1 for individual patients to enable patient monitoring.1Z-score maps are maps of the deviation of a qMRI metric for a specific patient from the healthy control793.2.1 MethodsDemographicsTwenty healthy children in Grade 4 participated in this study: sex = 16/4 male/female,mean age = 9.6, range = 9.2-10.3 yrs. Ethical approval was granted by the ClinicalResearch Ethics Board at University of British Columbia (UBC). Written, informedconsent for the participation in this study was obtained from the legal guardians of thechildren.MRIParticipants were imaged using a 3.0 Tesla MRI scanner (Philips Achieva 3T, Best, TheNetherlands) with an 8 channel head-coil. Imaging data included:1. 3DT1: IR-TFE, TR/TE/T I = 9.816/5.70/876.6 ms, shot interval = 3000 ms, ac-quired resolution = 1.1×1.1×1.1 mm3, 136 slices, whole brain coverage.2. MWI: 48-echo 3D GRASE, TR/∆TE = 1000/8 ms, acquired resolution = 1.5×1.8×4.4 mm3, reconstructed resolution = 1.1×1.1×4.4 mm3, 10 slices with theinferior border of the volume along the inferior edge of the genu/splenium of thecorpus callosum, covering a 211×181×44 mm3 field of view which was 38-47%of the total brain volume.3. DTI: diffusion-weighted SE-EPI, TR/TE = 3600/68 ms, b=0 (acquired at start) and1000 s/mm2, 15 diffusion directions, acquired resolution = 2.2×2.2×2.2 mm3,reconstructed resolution = 1.1×1.1×2.2 mm3, 60 slices, whole brain coverage.atlas, normalised by the healthy control standard deviation.80Data processingMWI data were corrected for stimulated echo artifacts using an extended phase graphalgorithm to estimate the refocusing flip angle in each image voxel (see Section 1.10.2for a brief explanation of the method). [164] Data were then fit with a multi-exponentialdecay using NNLS (in-house MatLab software which can be requested from: https://MRIresearch.med.ubc.ca/news-projects/myelin-water-fraction/) for forty T2 componentslogarithmically spaced between 15 ms and 2 s. [31] Maps of MWF, defined as the sig-nal fraction with T2 < 40 ms, were produced for each participant. These maps thenunderwent a novel non-local spatial regularisation to produce a smoother, more globallyconsistent metric map. [192] This method uses neighbouring voxels with similar T2 dis-tributions to the pixel of interest to denoise the MWF map. DTI data was motion andeddy-current corrected with FSL EDDY and Gaussian diffusion tensors were fit to thedata in each voxel using FSL DTIFIT which were used to produce FA, MD, RD and ADmaps for each participant (see Section 1.13.1 for the theory behind this). [193–196]Atlas creationAll twenty 3DT1 structural images were corrected for low-frequency bias and normal-ized to their mean intensity before being combined to create a representative pediatrictemplate using ANTs. [197] Template creation involved iterative rigid, affine, and sym-metric diffeomorphic normalisation transformation. The first echo (echo 1) of the MWIacquisition and FA map for each subject were registered to the corresponding 3DT1 scanusing an FSL affine registration with 12 degrees of freedom. [191, 198] The FA map wasused because of its excellent white matter/grey matter contrast. The affine transforma-tion created for the FA map was used to align the RD, MD and AD maps and the affinetransformation created for echo 1 was used to align the MWF map, to each participant’s3DT1 scans. All of the metric maps were then transformed to the template space using81Figure 3.6: The pediatric atlas creation process: metric maps were created fromDWI) and 48-echo GRASE data.the ANTs transformations previously produced (Figure 3.6 shows this process). Finally,voxel-wise metric mean and standard deviation maps were calculated, combining thedata from all twenty subjects.Regions of interestNine white matter ROIs which were covered by the MWI acquisition region, three com-missural and 6 bilateral, were chosen from the JHU White Matter Atlases (Figure 3.7).[6, 199, 200] The JHU template was registered to pediatric structural template usingFSL FNIRT. [166, 201, 202] The non-linear warp produced was then used to transformall nine ROIs to the pediatric template space where they could be applied to the metricmaps. The structural template was segmented using FSL’s FAST algorithm. [167] Thewhite matter mask produced was thresholded at a 90% white matter probability and used82Figure 3.7: The 3 commissural and 6 ROIs chosen for our study overlaid on the pe-diatric myelin water imaging data. The superior-inferior extent of the myelinwater imaging acquisition region can be seen.to mask the ROIs to include only white matter voxels. The ROIs were further maskedby the region of the brain covered by the MWI acquisition which fully encompassed thebody of the corpus callosum, and the superior and posterior corona radiata. All otherROIs had some voxels outside of the MWI acquisition region. Final ROIs used on boththe adult and pediatric MWI and DTI atlas analysis can been seen in Figure 3.7.83Statistical analysisAverage mean and standard deviation of MWF, FA, MD, RD and AD in each ROI wascalculated from the atlases (Table 3.4). The same ROIs which had been applied to thepediatric maps were used to analyze adult MWF from an atlas created using a 32-echo3D GRASE acquisition protocol collected on the same scanner from 50 healthy adults(mean age 25 yrs, range 17− 42 yrs, sex: 25 male/25 female). [184] Two-tailed t-tests were performed to compare pediatric MWF with adult MWF.2 DTI data was notavailable from this adult sample. Spearman correlations between pediatric MWF andDTI metrics and also between adult and pediatric MWF were examined.3.2.2 ResultsThe pediatric structural template and MWF and DTI atlases are publicly available asNIFTI files at: https://sourceforge.net/projects/pediatric-MRI-myelin-diffusion/. As canbe seen qualitatively in Figure 3.8, the grey matter/white matter delineation shown bythe MWI and DTI atlases were anatomically similar, however MWF appeared more ho-mogeneous throughout white matter than the DTI metrics, with the exception of veryhigh MWF in the corticospinal tracts. FA appeared much higher in the genu, body andsplenium of the corpus callosum and lower in the anterior and posterior corona radiata.ROI metric values (Table 3.4) confirmed visual observations, with pediatric MWF be-ing highest in the corticospinal tracts, superior corona radiata and superior longitudinalfasciculus while FA was highest in the genu, body and splenium of the corpus callosum.Standard deviation across participants in all metric maps was low throughout thebrain (less than 4% for MWF, 0.08 for FA, 0.07× 10−3 mm2/s for MD, 0.14× 10−3mm2/s for AD and 0.09×10−3 mm2/s for RD) and similar in most regions, indicative2“Two-tailed” means that this test is testing the alternate hypothesis that the correlation coefficient issignificantly different than zero in either the positive or negative direction.84Region Ped.ROI vol(mm3)AdultROI vol(mm3)AdultMWF(%)Ped.MWF(%)p value Ped. FA Ped. MD Ped. AD Ped. RDGEN 817 1158 6.0±3.1(52%)4.7±4.4(93%)0.180 0.54±0.07(13%)0.82±0.05(6%)1.40±0.12(9%)0.53±0.07(13%)BOD 7926 11860 8.8±3.2(36%)5.7±3.5(62%)0.001* 0.58±0.07(12%)0.84±0.07(8%)1.48±0.13(9%)0.52±0.08(16%)SPL 3596 5269 10.2±2.9(28%)6.6±3.3(50%)<0.001* 0.65±0.07(11%)0.80±0.07(9%)1.51±0.14(9%)0.45±0.09(20%)ACR 2779 3989 6.9±2.6(38%)4.8±1.6(33%)0.001* 0.37±0.06(17%)0.77±0.04(5%)1.08±0.08(8%)0.61±0.05(9%)SCR 9209 14846 12.1±2.9(24%)8.6±1.9(22%)<0.001* 0.46±0.06(14%)0.72±0.03(5%)1.11±0.08(7%)0.53±0.05(9%)PCR 4740 7182 9.6±2.5(26%)6.5±2.1(32%)<0.001* 0.44±0.08(17%)0.79±0.06(7%)1.18±0.10(9%)0.59±0.07(12%)CIN 2937 3446 5.3±2.7(51%)4.3±2.9(68%)0.187 0.44±0.08(18%)0.78±0.06(7%)1.17±0.11(10%)0.58±0.07(13%)SLF 7649 10864 11.8±2.8(24%)7.5±1.7(23%)<0.001* 0.43±0.07(17%)0.73±0.05(6%)1.08±0.09(9%)0.56±0.06(12%)CST 1059 1785 16.2±2.8(17%)11.7±2.1(18%)<0.001* 0.52±0.07(13%)0.72±0.03(4%)1.18±0.09(7%)0.49±0.05(9%)Table 3.4: ROI values given as mean± standard deviation (coefficient of variation (%)) for the adult (no. of participants=50)and pediatric (no. of participants=20) atlases, using custom ROIs restricted to the brain region covered by the MWIsequence. (* indicates statistical significance at the p < 0.05 level).85Figure 3.8: Five representative axial slices of the MWF, FA, MD, AD and RDatlas metric maps. Yellow box shows superior/inferior coverage of the MWIacquisition. MD, AD and RD are in units of 10−3 mm2s−1.86Figure 3.9: Single slices displaying MWF, FA, MD, AD and RD mean (top)and standard deviation σ (bottom). MD, AD and RD are in units of 10−3mm2s−1.of good anatomical alignment (representative slices are shown in Figure 3.9). DTI met-rics were more consistent across participants with FA, MD, RD and AD having a smallermean voxel-wise CoV than MWF. When the bilateral ROIs were analysed separately,two-tailed t-tests found no significant differences between left and right regions in anyof the ROIs in MWF, FA, MD, AD or RD.Adult ROI MWF values were between 23 and 58% higher than pediatric MWF,with the greatest difference in the body and splenium of the corpus callosum and thesuperior longitudinal fasiculus. This difference was significant in seven of the nineROIs investigated (Figure 3.10) but not in the cingulum and the genu of the corpuscallosum. There was a very strong correlation between the pediatric and adult MWF87Figure 3.10: (A) Adult and pediatric MWF slices for qualitative comparison. (B)Pediatric MWF (number of participants = 20, 9-10 years) was significantlylower than adult MWF (number of participants = 50, 17−42 years) in sevenout of nine of the ROIs investigated (∗∗∗p < 0.001, ∗∗p < 0.01). Percentageincreases are shown above the bars, ranging from 23-58% higher in the adultbrain. (C) There was a very strong correlation between adult and pediatricMWF.values (R2 = 0.957, p < 0.0001).At the α = 0.05 significance level, only MD (R2 = 0.486, p = 0.037) was signifi-cantly correlated with MWF in the nine white matter ROIs investigated (Figure 3.11b),while FA, AD and RD were not.88(a) MWF vs FA(b) MWF vs MD89(c) MWF vs AD(d) MWF vs RDFigure 3.11: Significant correlation between MD and MWF. Shading shows theestimated confidence interval for the linear regression. No significant corre-lation between FA, AD or RD and MWF. The regions of the corpus callosum(genu, body and splenium) do not follow the trend shown by the other ROIs,as they have high diffusion anisotropy but relatively low MWF.903.2.3 DiscussionTo our knowledge, this is the first MWF and DTI metric atlas, and corresponding struc-tural template, for children aged 9−10 years old. MWF had a slightly higher standarddeviation than the DTI metrics. This may be caused by the small myelin signal resultingin a lower SNR in the MWI acquisition. Alternatively, this may be reflecting a largernatural population variation in MWF. Standard deviation in all maps was higher in thecortical areas (bright rim in Figure 3.9) caused by larger registration error near the edgeof the brain which occurs due to the natural variation of gyri and sulci.We compared our atlas with the only other published MWI data from children inthis age range. Geeraert et al. [90] studied a pediatric cohort with a slightly higher meanage (11.5 yrs) and a larger range (8− 13 yrs) using a mcDESPOT acquisition on a 3Tesla GE Discovery system. The mcDESPOT-derived myelin volume fraction (VFm)values were consistently much higher (30-250% higher) than the T2-based MWF values,as has been previously reported. [27] We found an increase in MWF moving fromanterior to posterior of the corpus callosum, matching the variation in MWF measuredin adults. [184] In contrast, Geeraert et al. [90] found that the genu had the highestVFm. Histological studies of the corpus callosum have shown average fiber diameterincreases moving from the genu to the splenium; we measured an increase in MWFmoving in the same direction. [203, 204] When comparing VFm and T2-based MWF,the effect of exchange may play a role. It has been shown that smaller axon diametersand thinner myelin sheath results in more inter-compartmental proton exchange andtherefore smaller MWF so this may be contributing to our results. [53] mcDESPOT-derived MWF can be affected by magnetization transfer effects due to the use of short,high amplitude RF pulses which may be contributing to the much higher MWF valuesreported by mcDESPOT. [205]91As in previous adult studies, high MWF values were observed in the corticospinaltracts. This may be due to the intra-extracellular water signal which has been shown tohave a broader T2 range and is frequently split into two distinct peaks, suggesting thatintra- and extracellular environments have different T2 times in the corticospinal tracts.This broad peak can extend below the T2 = 40 ms MWF threshold and so interpretationsof MWF in this region should be made carefully. [170]Myelination during developmentThe fact that MWF measured in children aged 9−10 years was significantly lower thanin adults in most of the ROIs investigated was expected, as cognitive development andbrain maturation continue throughout the teenage years. [119, 125, 127] Similar myelinincreases during development are also seen using the mcDESPOT approach to MWI.[134, 136, 138]An interesting result is the very strong correlation between adult and pediatric MWFacross the nine white matter ROIs. During earlier development, histological studies haveshown a clear spatiotemporal pattern of myelination over time. Myelination begins in thecerebellum and brain stem before birth and progresses through the brain from posteriorto anterior and from the centre out. [206] However this strong correlation between MWFin adults and 9− 10 year old children suggests that, by this stage of development, thewhite matter ROIs maintain their relative myelination hierarchy into adulthood.There were some differences between the acquisition and processing protocols usedto create the adult and pediatric atlases. Data for both studies was acquired on the samePhilips Achieva 3 Tesla scanner, however the adult MWF atlas was created with 32-echo GRASE data with 10 ms spacing, whereas the pediatric atlas was created with48-echo GRASE data with 8 ms spacing. These two echo acquisition schemes havebeen contrasted and found to be comparable. [207] The adult data was acquired at920.99×0.99×5 mm3 resolution, reconstructed to 1×1×1 mm3 and so is slightly higherresolution. This should not have a large impact on MWF maps, although smaller voxelsizes will reduce partial volume effects. The range of T2 components used for fitting (15ms−2 s) and the MWF T2 range (15− 40 ms) were the same for both analyses but thepediatric atlas included a novel non-local spatial regularisation which is thought to givemore globally consistent MWF values while preserving fine structure detail. [192] Thisshould not affect the ROI values found. Finally, it is worth noting that the adult MWFROI values reported here are different from those reported by Liu et al. [184] althoughboth studies use the same adult MWF brain atlas. This discrepancy stems from thedifference in ROIs used; our ROIs were limited by the pediatric MWI acquisition regionso in some cases did not encompass the entire white matter region. We used these sameROIs for the adult data which enabled a direct comparison between the two groups.Complementary information from MWI and DTIMD was the only DTI metric found to be significantly correlated with MWF whenanalysing all nine regions. If a Bonferroni correction for multiple MWF/DTI met-ric comparisons was performed, reducing the significance threshold to p < 0.05/4 =0.0125, this correlation became non-significant. These results show that DTI and MWIaccess different aspects of the tissue microstructure and so should be used in conjunctionto characterize tissue.The three regions of the corpus callosum lay outside the linear relationship shown bythe other regions for MWF vs FA, AD and RD (Figure 3.11). The corpus callosum hadsignificantly higher FA than expected given its fairly low MWF (5-7%). This findingagrees with that of Ma¨dler et al. [51], who performed the same MWF/DTI comparisonin adults, and is thought to be due to the tightly packed, coherently oriented axons in thisregion which increase the diffusion anisotropy independently of the myelination. [51]93LimitationsThere are several limitations which should be considered when interpreting our study.1. Although reasonably sized for a pediatric study, our atlas has a relatively smallsample size which may make it less reliable for representing the whole populationof the 9−10 yr old age group.2. Our MWI sequence did not provide whole brain coverage. The 10 slices of MWIdata collected encompassed 38− 47% of the total brain volume and containedmany important white matter regions, however, some of our ROIs did not encom-pass the full white matter region, and there were brain regions beyond our field ofview which could not be investigated. More recent versions of the GRASE MWIsequence can provide full-brain coverage with a 3D or 2D sequence in under 10minutes. [24, 208]3. This study had mostly male participants (16 out of 20) and so we could not per-form meaningful analysis of sex differences in this age group. However, Geeraertet al. [90] investigated 17 white and grey matter ROIs and found no significantdifferences in any of them between male and female children aged 8-13 yrs. Thissuggests that the unequal sex ratio in our data does not reduce its utility for chil-dren of both sexes in the 9−10 year age range.4. Finally, the narrow age range of 9−10 years is both a benefit and a limitation. Thisatlas is only applicable for children in a small age range. However, both DTI andMWF metrics change rapidly during adolescence, and so combining data from arange of ages would make the atlas less accurate for any one particular age group.Combining 20 participants in such a narrow age range allowed the creation of areliable atlas for 9−10 year olds.943.2.4 ConclusionWe have created normative myelin and diffusion tensor atlases, with a correspondingstructural template, for children aged 9− 10 years. The atlas is available online foruse as a reference in pediatric neuroimaging studies (https://sourceforge.net/projects/pediatric-MRI-myelin-diffusion/). We found MWF in nine white matter regions was sig-nificantly correlated with MD at the p< 0.05 level, but not with FA, AD, and RD. Sevenof the nine ROIs had significantly lower MWF in the pediatric group than in adults, high-lighting the importance of age-specific control data.953.3 Future researchAn interesting next stage in the comparison between MWF and ihMTR in the healthybrain would be to investigate how T1D-filtering might change the strength or intercept ofthe correlation. A stronger correlation with stronger T1D-filtering would be a indicationthat T1D-filtering is increasing the myelin specificity.Another interesting area to investigate is the lack of correlation between MWF andihMTR in five of the white matter regions in this study. (The anterior corona radiata,hippocampal cingulum, genu of the corpus callosum, posterior thalamic radiation andsagittal stratum.) Further research could reveal which effects, such as microstructure orfibre orientation, are affecting the contrasts differently and contributing to the lack ofcorrelation.To clarify the results we found in grey matter, an important study to perform wouldbe to use simulated data with different signal levels and realistic noise, to see how MWFanalysis and ihMTR signal are affected as the quantity of signal approaches the noisefloor.An obvious next step in measuring MWF in children is to study a range of pediatricage groups. This would allow a finer grained investigation of the development of myelinthrough childhood and adolescence, and could also be added to the publicly availablenormative data which can be used for registration and reference in pediatric brain re-search.96Chapter 4The orientation dependence ofmyelin imaging metrics in whitematterWhite matter microstructure is naturally anisotropic, with bundles of aligned axons cre-ating a characteristic direction which varies in angle to B0. This orientation can affectqMRI contrasts through various mechanisms; measuring and mitigating qMRI orienta-tion dependence is a very active research area.MWF has been found to vary with white matter fibre orientation, perhaps due tothe orientation dependence of the underlying T2 relaxation. [209, 210] In vivo studiesreport that white matter tracts parallel to the B0 field have significantly higher ihMTRvalues than tracts perpendicular to the field; likely due to the oriented nature of dipolarcoupling in the myelin bilayers which wrap around axons. [2, 64, 65, 90]974.1 Orientation dependence in the healthy adult brainOur aim was to measure the orientation dependence of MTR, ihMTR and MWF in whitematter in the healthy brain. We wanted to investigate whether the diverse microstruc-tural properties of different white matter tracts might change the apparent orientationdependence.4.1.1 MethodsMRIParticipants were scanned at 3 Tesla (Philips Achieva) with an 8-channel receive-onlyhead-coil to obtain 3DT1, MT/ihMT and MWI as described in Section 3.1.1, and alsoDTI: SE-EPI, TR/TE = 5817/70 ms, resolution = 2.5×2.5×2.5 mm3, 55 slices, FoV =212× 212× 137.5 mm2, 1 b = 0 scan, 32 b = 900 scans in different directions, 6 mins30 secs).Data processingMT/ihMT and MWI processing are described in Section 3.1.1. DTI processing, whichis required to determine the orientation dependence, is described here. The DTI datawas eddy current corrected using FSL EDDY [193, 194] with outlier replacement andfit with diffusion tensors using FSL DTIFIT. [195, 196] A strict FA threshold of 0.5 wasapplied to restrict the voxels included and thereby reduce the effect of crossing fibreson the orientation dependence trends. The fibre angle to the main field for each whitematter voxel was calculated from the first eigenvector of the fitted tensor. Tract angleand metric data were read out from every voxel in the FAST-generated white mattermask. Tract angle was restricted to 0−90◦ and binned in 1◦ increments. Metric valuesfrom every participant across all of white matter were binned by either participant orwhite matter structure and then the mean metric value in each bin was plotted. Any bin98which contained fewer than 100 voxels was not plotted on the graph.Regions of interestRegistration and regions of interest are described in Section 3.1.1.4.1.2 ResultsAll three metrics displayed changes with fibre angle; this observation could be seenin each participant (Figure 4.1a) and across each white matter structure (Figure 4.1b).MWF and ihMTR were lower in white matter tracts perpendicular to B0 by approxi-mately 6% and 1% (metric units) respectively compared to parallel tracts. MTR had adifferent apparent orientation dependence, with a decrease of 0.5% at around 40◦, andhigher values at 90◦. Each white matter region had a distinct range and distribution ofangles shown in Figure 4.3. Most of the white matter structures had more voxels athigher angles.For MTR, the orientation dependence which was seen across the whole brain whenvoxels were binned by participant (Figure 4.1a) was only visible in some regions. Inthe angle range 20−50◦, the cerebral peduncles had a strong decrease in MTR withangle, while the posterior limb of the internal capsule had no change in MTR withangle. For 70−90◦, the majority of the regions, such as the splenium and genu of thecorpus callosum, the superior longitudinal and superior fronto-occipital fasciculus andthe retrolenticular part of the internal capsule had a strong increase in MTR with angle,while the body of the corpus callosum, the posterior and superior corona radiata had noapparent orientation dependence.Similar to MTR, ihMTR had a clear overall tendency but the gradient of ihMTRchange with angle varied for each white matter region. Only three regions had significantnumbers of voxels at small angles (10−30◦): the cerebral peduncle, the posterior limb99(a) Separated by participant(b) Separated by regionFigure 4.1: Apparent orientation dependence of MTR, ihMTR and MWF, separated by participant and region. Angles werecalculated using the primary eigentensor fit to DTI data. White matter voxels were binned by participant or by ROIand then by angle. Marker size was scaled by number of voxels at each angle. Angle bins which contained less than100 voxels in the ROI or participant were not plotted. Number of voxels in each angle bin for each ROI are shown inFigure 4.3.100Figure 4.2: The apparent orientation dependence of MTR, ihMTR and MWF foreach region shown separately.101Figure 4.3: Histograms showing the number of voxels in each angle bin for eachregion. Dotted line shows the cut-off for inclusion in the orientation depen-dence graphs in Figure 4.1.of the internal capsule and the superior corona radiata. All three of these regions had adecrease in ihMTR with angle but with very different slopes. At the other end, 50−90◦,there was hardly any variation with angle at all, only the anterior limb of the internalcapsule and the body of the corpus callosum showed a decrease in ihMTR with angle.MWF had the least consistent apparent orientation dependencies between regions.For example, in the range 20−40◦: in the posterior limb of the internal capsule, MWFdecreased steeply with increasing angle; in the superior and posterior corona radiata,MWF decreased more shallowly; in the cerebral peduncles, MWF did not vary withangle but then decreased; in the splenium of the corpus callosum, MWF did not vary102Figure 4.4: Investigating the trends of ihMTR and MWF with fibre angle and ap-proximate axon size in the corpus callosum and the cerebral peduncles/inter-nal capsules.with angle and finally in the superior longitudinal fasciculus MWF increased with angle.4.1.3 DiscussionWe measured how MTR, ihMTR and MWF varied with fibre angle relative to B0 acrosseach white matter region and participant for all three metrics. Previously, both ihMTRand MWF have been shown to be up to 25% higher in white matter fibres running parallelto the B0, compared to fibres running perpendicular. [64, 90, 209] The curves for eachparticipant followed the same general trend but were offset (Figure 4.1a), due to each103participant’s innate myelination, which can vary with many factors including age andeducation. [186] MWF and ihMTR followed a fairly similar pattern across fibre angles,with higher values at small angles and lower values near 90◦. This is in agreementwith previous in vivo ihMTR measurements [64, 90] and matched the MWF apparentorientation dependence measured by Birkl et al. [209] very well. MTR did not varywith fibre angle as much as ihMTR and MWF, which may be because MTR contrast isinfluenced by non-myelin macromolecules which do not have the same level of coherentalignment as the lipid bilayers in myelin.Pampel et al. [77] investigated the underlying mechanisms for orientation depen-dence in magnetisation transfer parameters. They modelled the orientation dependenceof the absorption lineshape for a fibre bundle, which is anisotropic due to dipolar in-teractions in the lipid bilayers of the myelin membrane. The lineshape in turn affectsthe saturation efficiency of the off-resonance pre-pulse and thereby the MT contrast. Ina previous study (described in Section 4.2), we found the changing linewidth to be thedominant mechanism determining ihMTR orientation dependence in an oriented pla-nar lipid bilayer sample under our experimental conditions. [2] However, the apparentorientation dependence of ihMTR in the healthy adult brain did not match this in vitrowork. We believe that the discrepancy is due to a combination of the ihMTR frequencyoffset changing the apparent orientation dependence curve and also the natural hetero-geneity of myelin across the brain. It is also true that this simple bilayer sample onlyincluded one type of lipid and no proteins and so does not reflect the full complicatedbiology of the myelin sheath.Orientation dependence in T2 has been extensively reported, but the mechanism isstill being investigated. Studies have suggested a role for susceptibility effects due tomyelin, vasculature, iron and dipole-dipole or magic angle effects. [209, 211–214] Thiscan affect MWF which is determined from the T2 spectrum with a fixed cut-off. Schyboll104et al. [215] measured a small orientation dependence in T1, which could also affectMWF.The most commonly used method to analyse orientation dependence involves pool-ing data from all white matter voxels by fibre angle and then averaging. [64, 90, 209] It isknown that microstructural parameters including axon diameter, density and alignment,myelin volume and thickness, vasculature and iron content vary considerably acrosswhite matter in the brain. If these parameters had no correlation with fibre orienta-tion, then pooling all voxels could average out the effect of microstructural differences.However, this is not the case in the brain, where many microstructural parameters aredifferent in projection fibres such as the corticospinal tracts (which are parallel to B0)than in most of the association fibres such as the superior longitudinal fasciculus (whichare mostly near perpendicular to B0). For example, axon sizes are larger in the corti-cospinal tracts. [216] The effect of crossing fibres is also likely to be non-homogeneousacross fibre orientations in the brain. Because of this, when the data from all regionsare averaged, we find an apparent orientation dependence which is a combination oforientation dependence of the MRI metric and the effect of the natural heterogeneityof microstructure within and between regions. This heterogeneity may affect the MRImetric directly (e.g. higher myelin volume increases MWF) or may change the shape ofthe orientation dependence curve (e.g. iron contributing to susceptibility effects).By separating the apparent orientation dependence into specific regions we restricteach curve to a smaller range of microstructural parameters to some extent, although ithas been shown that there can be significant variation in e.g. axon diameter, density,myelin thickness and MWF along fibres too. [204, 217] Investigating MTR, ihMT andMWF as functions of fibre angle separated by white matter region was particularly in-teresting, as we found that white matter regions spanning the same range of angles havevery different apparent orientation dependencies (Figure 4.1b and Figure 4.2). This105was seen in all three metrics but most distinctly in MWF, highlighting the fact that mi-crostructural properties such as myelin volume vary between white matter fibres and arecontributing to the apparent orientation dependence curve.We were expecting to measure differences in apparent orientation dependence be-tween the metrics, as the mechanism for each metric is different and so will have dif-ferent contributing factors. However, similarities could hint at the microstructural prop-erties in common underlying the apparent orientation dependence. When averaged asin Figure 4.1a, ihMT and MWF have similar apparent orientation dependence, suggest-ing that differences in myelination are contributing to the observed trends with angle.Separating the apparent orientation dependence by the white matter fibres, as we havedone in this study, can begin to help disentangle real myelin differences from orien-tation dependence caused by the contrast mechanism. We saw qualitatively that theapparent orientation dependence within one fibre across different metrics was rarelythe same. ihMTR and MWF had similar apparent orientation dependence curves inthe posterior and anterior limbs of the internal capsule, cerebral peduncles and supe-rior fronto-occipital fasciculus, though not in other regions. As MWF and ihMTR aremyelin-specific metrics, this could suggest that these curves are being driven by myelinvariations. MTR and MWF had good agreement on apparent orientation dependence inthe genu, body and splenium of the corpus callosum and the posterior thalamic radiation,though not in the other regions. ihMTR and MTR had almost no agreement on apparentorientation dependence.ihMTR decreased more consistently with angle across the white matter regionswhich suggests that ihMT has a greater true orientation dependence, likely caused by theorientation dependence of the lipid lineshape and T1D. Future myelin content measure-ments using ihMTR should include a correction for the fibre orientation effects. Fromthe large variation in apparent orientation dependence across regions, it appears that106MWF may have a weaker innate orientation dependence and so the apparent orientationdependence is more greatly affected by myelin and other microstructure differences.One major limitation to this study is that the DTI tensor fitting procedure used to cal-culate white matter fibre angles does not consider crossing fibres in a voxel. This is thesame method used in other studies of orientation dependence, however it introduces an-other confounding factor into the apparent orientation dependence curves. Some of thedifferences in apparent orientation dependence between regions seen in Figure 4.1b andFigure 4.2 could also be due to the incorrect angles resulting from fitting one principleeigentensor to a region with crossing fibres. To somewhat mitigate this, we restricted theorientation dependence analysis to white matter voxels with FA > 0.5, thereby reducingimpact of crossing fibres on the voxels studied.4.1.4 ConclusionAll three metrics showed some variation with fibre angle; MWF and ihMTR were lowerin white matter fibres perpendicular to B0 while MTR was lowest around 40◦ to B0,and highest perpendicular to B0. Separating the orientation behaviour by white matterregion revealed a large discrepancy between the apparent orientation dependencies ofsome white matter regions with voxels in the same angle range. This lends weight to thetheory that real differences in myelination and other microstructural properties withinwhite matter confound the apparent orientation dependence measured using this methodin vivo in the brain.107Figure 4.5: (a) Definition of the bilayer orientation angle α , with respect to ex-ternal magnetic field B0. (b) The myelin bilayer phantom in the NMR tube,illustrating the POPC lipid bilayers between stacked glass slides.4.2 Causes of orientation dependence in ihMTGiven previous in vivo reports of ihMTR orientation dependence in white matter, our ob-jective was to investigate the cause of this effect by measuring how M2, R1D and ihMTRvary with orientation. To simplify the experiment we used an oriented planar phospho-lipid bilayer sample of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), themost prevalent phosphocholine lipid in the myelin sheath [218], and measured M2, R1Dand ihMTR over a range of angles. We then used our experimental results to simulatethe orientation dependence of ihMTR and R1D in a cylindrical bilayer system like themyelin sheath.4.2.1 MethodsPOPC bilayer experimentBilayer samples were prepared using 2 µl of POPC dissolved in chloroform (20 mg/mlconcentration, Avanti Polar Lipids Inc) pipetted onto eighteen sterile 20×4×0.17 mmglass coverslip pieces. The slides were dried under vacuum for two hours, stacked, andplaced inside a 5 mm diameter NMR tube and hydrated in a 70% humidity chamber108for 18 hours at 50◦C. The NMR tube was sealed with an o-ring cap and secured in agoniometer probe which rotated the sample in a Varian Unity Inova 400 MHz (9.4 Tesla)NMR spectrometer (Figure 4.5). All experiments were performed at room temperature.M2: A single hard 90◦ pulse (duration 2.3 µs) was applied before signal acquisition tomeasure M2. The glass slides were aligned approximately perpendicular to B0 (α = 0)by eye and then a more precise angle was estimated by minimising the Normalised RootMean Squared Error (NRMSE) between the data and the quadratic dipolar couplingstrength relation (Equation 1.26) which was fit to the data. The sample was rotatedthrough α = −4−180◦ in 8◦ increments (i.e. −4◦, 4◦, 12◦, 20◦. . . 180◦). Both thefrequency of the water resonance and the water lineshape varied with angle due to thesusceptibility of the non-symmetric glass sample substrate. [219] The water resonancewas found to have an orientation dependence of approximately 1300Hzcos2α .In this experiment the transmitter frequency was varied to match the water frequency.A small background signal originating from the probe was removed by acquiring datawithout the sample present and subtracting this from the data. An exponential decaywas fit to the FID over the range 400−500 µs, by which time all of the lipid signal willhave decayed, to subtract the water signal from the whole FID. The remaining signalover the range 8−120 µs was then fit withS = S0(1− M2t22!+M4t44!− M6t66!) (4.1)using a trust region reflective algorithm in Python to extract the value of M2. This rangewas chosen because the first 4 data points were dominated by background, and after 120µs the lipid signal had decayed into the noise.109(a) Adiabatic Demagnetization in the Rotating Frame/Adiabatic Remagnetiza-tion in the Rotating Frame (ADRF/ARRF) pulse sequence used to measure R1D (τ =1 ms, δ = 10 µs –0.1 s in 15 equally spaced intervals). φ was cycled between x and-x to suppress water signal remaining in the x-y plane following the demagnetizationwithout disturbing the dipolar order.(b) The three pulse sequences (with zero, single and dual offset pre-pulse) usedto measure ihMTR (τ = 50 ms, δ = 0.5 ms).Figure 4.6: Pulse sequences for the NMR POPC bilayer experiments.110R1D: An ADRF/ARRF experiment was used to measure R1D for α = 0-88◦ in 4◦ incre-ments. A 90◦ pulse excited Zeeman magnetisation which was then spin locked by phaseshifting the RF by 90◦. Transfer to the dipolar reservoir was carried out by adiabaticallyramping-down the RF amplitude (Figure 4.6a). Following a relaxation delay, dipolar or-der was transferred back to observable Zeeman magnetization via a symmetric ramp-upRF pulse. R1D was determined from a single exponential decay fit to the non-aqueoussignal integral (evaluated over 32 kHz, excluding 1.5 kHz centered on the water peak).The fitting was performed with a NNLS algorithm. The decay was deemed “unable tobe fit” if the R1D estimate was negative.ihMTR A ‘continuous wave’ ihMT acquisition using single frequency and sine-modulateddual frequency pre-pulses (±8 kHz from water resonance, τ = 50 ms duration, band-width = 24 Hz, δ = 0.5 ms delay before data acquisition, B1rms = 14.3 µT/610 Hz,Figure 4.6b) was performed for α = 0−98◦ in 8◦ increments. The FID was read outwith a 2 µs dwell time and 500 repetitions which were averaged to improve SNR. Eachof S0, S+, S−, and S± took 10 minutes to acquire at each angle. This larger angular stepsize was used, compared to the 4◦ steps used for R1D, because the ihMT measurementstook substantially more time to acquire, and because the fibre model described belowcould use interpolations of the results, which appeared to be smoothly varying. The sin-gle frequency pre-pulse amplitude was 1/√2 of the dual frequency pre-pulse amplitude,to match the total power deposited. Aqueous ihMTR was calculated from signal inte-grals over 0.5 kHz centered on the water resonance frequency, and non-aqueous ihMTRwas calculated from signal integrals over 32 kHz, excluding the central 1.5 kHz centeredon the water resonance frequency using Equation 1.21.111White matter fibre modelIn the myelin sheath, the lipid bilayers wrap around the central axon concentrically. Inorder to relate our findings to the orientation dependence of ihMTR seen in the brainin vivo, we used a simple cylindrical model for the white matter fiber geometry (Fig-ure 4.7). Defining β (the angle between the fibre direction and B0), nˆ (the normal to themyelin bilayers wrapping around the axon) and θ (the angle between nˆ and the x-axis):B0 = B0(sinβ ,0,cosβ )nˆ = (cosθ ,sinθ ,0)(4.2)α , the angle between nˆ and B0 is given by:cosα =B0 · nˆB0= sinβ cosθ (4.3)We applied our experimental bilayer data in this geometry to estimate the orientationdependence of R1D and ihMTR in a cylindrically symmetric myelin sheath.To calculate R1D for the fiber model, the closest α for which R1D decay data had beencollected was determined for every α(β ,θ), i.e. the nearest multiple of 4◦. The signaldecay was averaged for all θ in 0.1◦ intervals which gave an averaged spectrum decayfor each β . The decay for each β was then fit with a single exponential to obtain R1D. Ina similar manner, the planar bilayer ihMTR data was interpolated using quadratic splineinterpolation to obtain a smooth function for ihMTR over all α = 0−90◦. Using the samegeometry, ihMTR(α(β ,θ)) for each θ from 0−360◦ in 0.1◦ intervals were summed toobtain an average ihMTR(β ). The effect of fiber dispersion on the resulting R1D andihMTR was investigated by introducing a Gaussian distribution of β with varying widthto the model.112Figure 4.7: The geometry used to define the fiber model. The fiber direction isat an angle β to B0. Lipid bilayers in the myelin sheath wrap around thefiber with the bilayer normal pointing out in the x-y plane away from thez axis. The vector normal to the myelin bilayers varies over the full circle(θ = 0−360◦). The angle between B0 and the bilayer normal vector is α .4.2.2 ResultsPOPC bilayer experimentM2: Representative FIDs from the aligned POPC bilayer sample are shown in Fig-ure 4.8. The orientation dependence of M2, is shown in Figure 4.9a. Angles −4−180◦were collected, and then mirrored around the 90◦ symmetry axis. The orientation de-113Figure 4.8: Representative FIDs from the aligned POPC bilayer sample at differentangles to B0.pendence of M2 corresponded to2.83×108Hz2(12(3cos2α−1))2+2.69×108Hz2 (4.4)with χ2v = 4.4, NRMSE = 7.3%.R1D: Decay curves from R1D measurements are shown in Figure 4.10. For angles52−56◦, the dipolar order lipid signal was negligible compared to the noise. This meantan exponential signal decay could not be fit to the data. R1D had a maximum (R1D =59 s−1, T1D = 17.0 ms) near 0◦ and an apparent minimum at the magic angle. The R1D114(a) M2 of the non-aqueous signals. Standard deviations were calculated from thecurve fitting.(b) R1D of the non-aqueous signals. Standard deviations were calculated fromthe curve fitting.115(c) ihMTR measured from the aqueous and non-aqueous signals. Standard devi-ations were calculated from the noise in each signal integral and propagated throughEquation 1.21. For the aqueous ihMTR, and the non-aqueous ihMTR at orientationslarger than 40◦, these errors are too small to be seen on the graph.Figure 4.9: Orientation dependence of M2, R1D and ihMTR in aligned POPClipids. The magic angle (α = 54.7◦, root of the dipolar coupling strengthangular dependence) is marked with red dashed lines.orientation dependence in Figure 4.9b approximately followed48.2s−1(12(3cos2α−1))2+7.2s−1 (4.5)with χ2v = 4.7, NRMSE = 14.9%.ihMTR: The orientation dependence of ihMTR is shown in Figure 4.9c. The aqueousihMTR had a maximum (15.0%) at small angles, decreasing to a minimum (0.4%) near116Figure 4.10: Relaxation of dipolar order in aligned POPC. Decay curves and fits to the data are shown at each angle. Errorbars were calculated from the noise in the signal integral at each angle.117the magic angle and approximately followed16.3%(12(3cos2α−1))2+0.3% (4.6)with χ2v = 19022 and NRMSE = 22.8%. The non-aqueous ihMTR had a maximum(29.3%) at small angles, a minimum (1.6%) near the magic angle and followed31.1%(12(3cos2α−1))2+1.4% (4.7)with χ2v = 182, NRMSE = 23.8%.White matter fibre modelThe ihMTR and R1D values measured when rotating the planar bilayer were used ina simple cylindrical model for the myelin sheath, in order to compare values to theorientation dependence of ihMTR seen in vivo. The R1D values for the fibre model weremoderated by the averaging of the different bilayer orientations contributing to each fibreorientation, ranging from a minimum (11.1 s−1) near the magic angle and a maximum(23.2 s−1) at β = 90◦. The cylindrical fiber model also resulted in much lower ihMTRvalues, ranging from a minimum (1.5%) at β = 40◦ to a maximum (6.0%) at β = 90◦(Figure 4.11). As β increased towards 90◦, the range of α increased so there was a muchgreater range of ihMTR/R1D values being averaged for each β (Figure 4.11b).Adding dispersion to the fiber model generally smoothed the orientation dependencecurves. In both ihMTR and R1D, fiber dispersion resulted in decreased values around β= 0◦ and β = 90◦.118(a) Orientation dependence of R1D.(b) Orientation dependence of aqueous ihMTR. The inset shows the increasingrange of α being averaged over for each β as β increased.Figure 4.11: Orientation dependence of R1D and ihMTR for the fiber model simu-lation.119(a) Orientation dependence of R1D.(b) Orientation dependence of aqueous ihMTR.Figure 4.12: Fiber model simulation including a Gaussian distribution of fiber di-rections with widths of 10◦ and 20◦ around the main fiber direction.1204.2.3 DiscussionWe measured the variation of M2, R1D and ihMTR with orientation using an orientedphospholipid bilayer sample. Along with extensive literature reporting oriented lipidsamples prepared this way, we take the variation in M2 as confirmation that the major-ity of the lipids were coherently aligned. [220] The correspondence between M2 andEquation 1.26 suggests that orientation dependence of the breadth of the spectrum isdominated by dipolar couplings. The M2 values measured here are in good agreementwith previous measurements of pure phospholipid multi-bilayer systems in the liquidcrystalline phase. [221] As seen in Figure 4.9a, 62% of the maximum M2 varies withorientation. We believe that the remaining 38% of M2 can be attributed to residual dipo-lar couplings, B0 inhomogeneity and imperfect lipid alignment. The error bars shown inFigure 4.9a are derived from the M2 fitting procedure and appear to slightly underesti-mate the true errors, as judged by the smoothness of the resulting fit values. This is notsurprising given the challenges associated with removing the water signals and subtrac-tion of background, which may not be reflected in the errors derived from the FID fits.The reduced chi-squared value of the M2 fit χ2v = 4.4 is consistent with the conclusionthat the errors are modestly underestimated; a doubling of the error bar size would yieldχ2v ≈ 1.The SNR of the dipolar order decays, especially near the magic angle, are relativelylow, as shown in Figure 4.10. R1D was not measurable near the magic angle where weakdipolar couplings minimize the available dipolar order. Some of the decays show somemodulations, possibly related to imperfect suppression of the water. The fits shownare to monoexponentials, the use of stretched exponentials did not improve the fits sig-nificantly. Relaxation rates found from fits are shown in Figure 4.9b. With the SNRavailable from these experiments we found that the R1D orientation dependence is qual-121itatively described byc0(12(3cos2α−1))2+ c1 (4.8)as would be expected if the relaxation is dominated by slower motions in the presence ofrapid rotation about the membrane axis, [80, 222] but make no quantitative conclusion.ihMTR was maximum at α = 0◦, where the bilayers were aligned perpendicularto B0 and minimum near the magic angle despite the fact that R1D is also minimizedthere. Similar to R1D, ihMTR orientation dependence was qualitatively described byEquation 4.8. We would expect a low R1D to lead to increased ihMTR; the dipolarorder relaxing more slowly will increase the difference between the single and dual off-set experiments. However, this was not evident in our results. Instead, the observedroughly quadratic relationship between ihMTR and the dipolar coupling strength mayreflect the dual effects of the size of the dipolar reservoir changing with dipolar couplingstrength and the saturation efficiency of the off-resonance pre-pulse on the changinglipid linewidth. ihMTR is naturally greater when the dipolar reservoir is larger. ihMTRalso depends on linewidth, as the ±8 kHz off-resonance pre-pulses have little effecton the narrow spectra of lipids near the magic angle. These considerations providea qualitative explanation of the fitting function used in Figure 4.9c. Deviations fromEquation 4.6 in Figure 4.9c are most obvious near α = 0◦ and α = 90◦ and suggeststhat Equation 4.6 overestimates the ihMTR at these limits. Equation 4.6 is not intendedto be a quantitative model however, as it incorporates only some aspects of the physicsinvolved. A quantitative model would account not only for the size of the dipolar reser-voir but also on the lipid saturation efficiency, which in turn depends on the amplitudeof the lipid line at the pre-pulse frequency, g(ω). We suspect that the omission of theseeffects from the fitting equation are responsible for the deviations between the fit andexperiment in Figure 4.9c. In this experiment, lipid linewidth and dipolar reservoir size122appeared to have a much greater effect on ihMTR than R1D. This is likely to be becausethe high pre-pulse amplitude resulted in a fast rate of magnetisation flow between theZeeman and dipolar reservoirs so that differences in R1D were not a significant factor inihMTR orientation dependence. [68]The ihMTR orientation dependence calculated from the fiber model is opposite tothat seen in brain white matter. In vivo in the brain, ihMTR had a measured maximumat β =0◦ (parallel to B0) and a minimum at β =90◦ (perpendicular to B0). [64] Someof this discrepancy could be attributed to real differences in myelination between brainwhite matter structures. This hypothesis is supported by the agreement between MWFand ihMTR orientation dependence in Section 3.1 and by the fact that the model bi-layer orientation dependence showed a better correspondence to the ex vivo spinal cordihMTR measured by Girard et al. [65] The orientation dependence in their experimentwith a larger (13 kHz) pre-pulse offset best matched our results with a pre-pulse off-set of 8 kHz. We hypothesise that the lipid linewidth in ex vivo spinal cord is greaterthan in our phospholipid bilayer sample. This may be due to the presence of cholesteroland other molecules in the ex vivo tissue which stiffen the bilayers, resulting in a widerlipid linewidth and an equivalent effect to our 8 kHz offset at 13 kHz. We note thatpre-pulse amplitude and duration were not matched between our study and Girard et al.[65] and these factors will also affect the ihMTR. Girard et al. [65] had a higher energydeposited, which would be expected to increase the ihMTR. For their in vivo brain study,Ercan et al. [64] used a 7 kHz offset, perhaps resulting in an ihMTR less affected by theorientation dependence of the linewidth.The choice of pre-pulse amplitude and duration used here (B1rms of 14.3 µT for aduration of 50 ms) differ from values typically chosen in vivo. These choices were madebased on simulations [68] which show that in non-aqueous proton pools, the ihMTR ismaximized with a shorter, stronger pre-pulse. The longer pre-pulse irradiation times123typically used in vivo allow for the slower process of equilibration with the aqueoustissue components. Due to the much smaller aqueous pool and intimate coupling withthe lipids here, such long equilibration times are not needed. Use of such strong B1 invivo would require a very low duty cycle to reduce the Specific Absorption Rate (SAR)to acceptable levels, which would likely be impractical. However, it has been shownpreviously that decreasing the duty cycle for the pre-pulse irradiation increases the ihMTeffect. [87] Provotorov theory, on which the understanding of ihMT is based, is valid inthe limit where ω1 ≪ ωD, which remains true everywhere except at orientations withina few degrees of the magic angle.As expected, adding fiber dispersion to our model introduced a smoothing of theorientation dependence curves which trended towards no orientation dependence at allas the fibers became less aligned. Intravoxel axon dispersion varies greatly in differentbrain areas and estimates from histology vary, for example 14.6◦ for the human corpuscallosum [223] and 10−30◦ for the mouse genu [224]. It may be that further, moredetailed, measurements of fiber orientation in brain, incorporating crossing fibers anddispersion and correlated with ihMTR, would help to explain the differences betweenthe in vivo ihMTR and our results.In the first study reporting ihMTR region of interest values in the brain, Varma et al.[59] compared the splenium of the corpus callosum, which is perpendicular to B0, andthe posterior limb of the internal capsules which are at approximately 45◦ to B0. Theyfound that at a high offset frequency (12− 15 kHz), the splenium had a higher ihMTRthan the internal capsules, whereas at a lower offset frequency (3− 10 kHz) this rela-tionship was reversed. Although these are just two data points, the variation in relativeihMTR of these two regions would support the claim that the difference between our re-sults and those of Ercan et al. [64] is due to the difference in pre-pulse offset frequency.When the pre-pulse offset frequency is low, the relative ihMTR follows the orientation124dependence shown by Ercan et al. [64] with perpendicular structures having the low-est ihMTR, but when the offset frequency is high the orientation dependence is closer towhat we found using our bilayer model, with perpendicular structures having the highestihMTR. We hypothesise that the size of the pre-pulse offset may change the weightingof the effect of linewidth versus R1D on the ihMTR.There are several limitations in the comparison between the simple fiber model pre-sented here and ihMTR measured in vivo:1. Our planar bilayer sample only included one type of lipid and no proteins, whichdoes not reflect the complicated biology of the myelin sheath.2. We fit single exponentials to our decay data to find R1D values at each orien-tation. Carvalho et al. [67] found that multilamellar vesicles of lipids (POPC,total cerebrosides and cholesterol) had Jeener echo decays better fit with multi-exponentials.3. We did not consider the effects of lateral lipid diffusion around the sheath. Anapproximate calculation using a diffusion coefficient D = 8 µm2/s [225] and awhite matter fiber circumference of approximately 1 µm (human corpus callo-sum) [204], suggests lipids could diffuse significant distances around the fibercircumference during the 50 ms ihMT pre-pulse.4. Exchange between water and lipid protons occurs on a 10− 100 ms timescale,primarily via proton exchange between water and the hydroxyl protons in lipidheadgroups, further complicating the dynamics of the system. [68]4.2.4 ConclusionLipid M2, R1D and ihMTR, measured in a phospholipid bilayer sample, vary consider-ably with orientation to B0. M2 followed the expected orientation dependence stemming125from the dipolar coupling Hamiltonian, ĤD (Equation 1.22). R1D is minimized near themagic angle. ihMTR depends on both linewidth and R1D, with linewidth effects dom-inating for sine-modulated dual frequency pulses. Using the planar bilayer results, wecreated a cylindrical fiber model and found that, for our experimental conditions, bothihMTR and R1D were maximum at β = 90◦, which most closely aligns with results foundin a study on ex vivo spinal cord with a larger pre-pulse offset. We hypothesise that thesize of the pre-pulse offset may change the weighting of the effect of linewidth versusR1D on the ihMTR. Comparison of absolute ihMTR values in brain white matter tractsat different angles to the B0 field should be made carefully with orientation dependencein mind.1264.3 Future researchThe only way to truly disentangle the factors contributing to the apparent orientationdependence is by rotating white matter fibres inside the scanner, which is hard to doin vivo. Tax et al. [211] used a tiltable RF coil to obtain data with the head at 0◦ and18◦ and fit the R2 decay for intra and extra-axonal water (separated by the anisotropyof diffusion), finding only a modest change in R2 of 5% for intra-axonal R2 and 14%for extra-axonal R2. It is noteworthy that a recent study demonstrated no orientationdependence for white matter MWF in rodent brain when changing the orientation of thebrain with respect to the magnetic field. [226]Another important next step would be to, where possible, systematically vary mi-crostructural parameters, and then rotate white matter fibers to see how axon diameter,density and alignment, myelin volume and thickness, vasculature and iron content af-fect the orientation dependence. Weber et al. [227] analysed the orientation dependenceof R∗2 = 1/T∗2 and myelin water fraction in neonates, who have naturally low levels ofmyelin and iron in the brain at birth, and found that R∗2 was much less affected by fibreorientation in the neonate brain, highlighting the importance of myelin and iron in themechanism for orientation dependence. [227]The study described in Section 4.1 only used a 30-direction DWI sequence and socould only reliably fit one diffusion direction to the data from each voxel. Future exper-iments could use a higher angular resolution DWI sequence to disentangle the effect ofcrossing fibres.Finally, gold standard measurements of microstructural parameters, from histolog-ical studies or orientationally independent diffusion models, could be compared to theorientation dependence in different white matter fibres to begin to build a model of thefactors contributing to the apparent orientation dependence.127From the experiments in Section 4.2 we hypothesise that the pre-pulse offset maychange the weighting of the effect of linewidth versus R1D on the ihMTR orientationdependence. Future experiments should investigate the interplay of these two factorsby varying the frequency offset. Detailed modelling of the fibre bilayer, including thecomplicated biology of the myelin sheath, lateral lipid diffusion and exchange of waterand lipid protons will be vital to understand the causes of ihMTR orientation dependencein more detail.128Chapter 5Applying myelin imaging to humantraumatic spinal cord injuryA significant problem in the diagnosis and treatment of traumatic SCI is the heterogene-ity of secondary injury which makes it difficult to predict neurologic outcome. Imagingbiomarkers specific to myelin loss and inflammation after traumatic SCI would enabledetailed assessment of the pathophysiologic processes underpinning secondary damageto the cord. Such biomarkers could be used to stratify injury severity and better informprognosis for neurologic recovery. While much work has been done to establish MRIbiomarkers for SCI in animal models, the relationship between imaging findings andthe underlying pathology has been difficult to discern in human traumatic SCI due to thepaucity of human spinal cord tissue. Data from the ISCIB provides a unique opportunityto investigate the sensitivity, specificity and utility of qMRI metrics in human traumaticSCI.1295.1 The International Spinal Cord Injury BiobankThe International Spinal Cord Injury Biobank https://www.sci-biobank.org/ houses spinalcords donated by individuals who died at Vancouver General Hospital after suffering atraumatic SCI. Consent was obtained in accordance with the local clinical research ethicsboard. These individuals have a baseline neurologic examination to document the clini-cal severity of their neurologic impairment according to the ISNCSCI scale. [148] Theyalso undergo a conventional in vivo MRI as part of their clinical care. The majority ofthe patients in the ISCIB are older adults, who sustained their injury in a fall.Post-mortem, the entire spinal cord was retrieved and immediately fixed in 10%formalin. The time between death and cord extraction/fixation (Post-Mortem Interval(PMI)), is given in Figure 5.1, along with the time between injury and death (Injury-Death Interval (IDI)), and the time between submersion in formalin and MRI acquisition(Fixation Interval (FIX)). Formalin-fixed cords were then cut into approximately ten 4.5cm sections to match the length of the RF coil used in the 7 Tesla MRI scanner forsubsequent imaging experiments.MRIFor each cord, the formalin-fixed 4.5 cm segments, centred at injury epicentre, wereimaged at 7 Tesla (Bruker Biospec) using a 35 mm inner-diameter quadrature volumecoil at room temperature to obtain:1. T2-weighted RARE for anatomical identification: slices = 45, resolution = 0.1×0.1× 1 mm3, TRTR/TE = 3101/15 ms, TE effective = 30 ms, Scan time = 3 m18s.2. Diffusion: multi-shell 3D diffusion-weighted SE-EPI, TR/TE = 250/41.21 ms,resolution = 0.15× 0.15× 1 mm3, six b = 0 scans, 5 shells with b = 500, 1000,130Figure 5.1: Demographics and injury details for the eight patients from the ISCIB included in the studies in Chapter 5 of thisthesis. The ISNCSCI scale AIS grades are described in Section 2.5. Cases are numbered based on shortest to longestinjury-death interval. Case 2 had a pacemaker which was contraindicated for MRI and so a pre-surgery CT image isincluded in this figure instead. MRI/CT were obtained post-surgery (Case 3, 8) and pre-surgery (Case 1,2,4,5,6,7).1312000, 3500, 5000, 7000 s/mm2 and 6, 15, 24, 42, 60, 80 directions respectively,distributed uniformly by a Spherical Code optimization algorithm, total scan time= 1 hr 27 min. [228]3. MWI: 3D multi-echo SE, TR/TE = 1000/6.5532 ms, resolution = 0.3×0.3×1.5mm3, 48 echoes, total scan time = 1 hr 8 min. Note: Cases 5 and 7 had acquisitionissues and were not included in the MWI analysis in Section 5.2.4. ihMT This data was obtained with both a 2D and a 3D sequence, and with andwithout T1D-filtering, as sequence development progressed. Note: Cases 2 and 3had a different ihMT protocol which was not harmonisable with the others and sowere not included in the ihMT analysis in Section 5.2.• 2D: single slice FLASH, TR/TE = 50/3.95 ms, resolution = 0.18× 0.18mm2, slice thickness = 1 mm, two slices located 9 mm above and belowinjury epicentre.• 3D: segmented 3D FLASH, 4 readouts per saturation train, TE = 2.888 ms,resolution = 0.36×0.36×1.5 mm3, ihMTR with no T1D-filtering (ihMTRuf)= 15 m 32 s, ihMTR with T1D-filtering (ihMTRf) = 18 m 54 s.• Frequency offset = ±8 kHz.• ihMTRuf: 2 cosine-modulated 1ms pulses, TR = 50 ms.• ihMTRf: 12 frequency-alternated 3.3 ms repeating pulses, TR = 100 ms.Non-local mean denoising The “local mean” method to denoise an MRI pixel involvestaking the average of pixels in the original pixel’s spatial vicinity which can lead tounwanted smoothing of sharp edges. Non-local mean denoising only takes the averageof pixels that are similar to the original pixel in intensity. There is still a search radius132Figure 5.2: The tissue processing which occurred after 7 T MRI, allowing accuratematching between histology and MRI slices.around the current pixel, but the algorithm ignores pixels which have intensities thatare too different. Normal denoising uses an average of spatial neighbours, whereasnon-local mean denoising uses an average of “intensity neighbours”, thereby preservingdetails of the image. In this study, non-local mean denoising was used to pre-process allMRI data. [229]MRI metric map processing Diffusion data were susceptibility and eddy current cor-rected (FSL TOPUP and EDDY) [194, 230] and fit with DTI, DKI (DESIGNER pipeline)[231] and DBSI.[103] ihMTR and inverse inhomogeneous Magnetisation Transfer Ratio(ihMTRinv) maps were created in MatLab using Equation 1.28. [232]HistologyThe anatomical T2-weighted scans were used to create a custom 3D-printed mould foreach cord piece with knife slots aligned with the caudal edge of every other MWF sliceor every third DTI slice (see Figure 5.2(B)). Using this mould, the cords were cut into3 mm slices and embedded in paraffin. 5µm-thick formalin-fixed paraffin-embedded133sections underwent histological staining for myelin lipids (LFB) and immunohistochem-istry for activated microglia and macrophages (Class II MHC and CD68) using the meth-ods described below.1. Tissue underwent deparaffinization and rehydration.2. Tissue was microwaved for 10 minutes in antigen retrieval buffer (0.01 M sodiumcitrate buffer), pH 6.0.3. Tissue was allowed to cool for 20 mins, rinsed in dH2O and then rinsed in 1XTRIS-buffered saline with Tween (TBST).4. Endogenous peroxidases were blocked with 3% H2O2 in 50% methanol.5. Tissue was rinsed in distilled water (dH2O) and 1X TBST again to remove re-maining H2O2.6. Protein blocker (2.5% normal horse serum) (ImmPRESS Kit, Vector LaboratoriesInc., Burlingame, CA) was applied and then drained from the slides.7. Diluted primary antibody (anti-CD68 antigen: Agilent Dako (Santa Clara, CA)Cat M0814, Clone KP1, antibody concentration 185 mg/l, dilution 1:60) or HLA-DP, DQ, DR antigen (Class II MHC): Agilent Dako Cat M0775, Clone CR3/43,antibody concentration 77 mg/l, dilution 1:50) was applied and incubated overnighton a plate shaker.8. Negative controls consisted of sections incubated with mouse IgG in the sameconcentration as the respective primary antibody.9. Tissue was rinsed 3 times in 1X TBST.134Figure 5.3: High resolution images of LFB staining for myelin phospholipids.Both slices are from one slide from Case 3, 9mm rostral to the epicentre,displaying an area of high and an area of low LFB staining.10. ImmPRESS (Peroxidase) Polymer Horse Anti-Mouse IgG Reagent (Impress Kit,Vector Laboratories) was applied and incubated for one hour, followed by washingthe slide three times in 1X TBST.11. To develop the antigen-antibody reaction, tissue was incubated for 5 minutes inVector NovaRED substrate peroxidase Kit.12. The slides were dehydrated and covered with a coverslip.After staining, the slides were digitised at 0.25 µm/pixel (40×) resolution using a LeicaAperio AT2 scanner.135Figure 5.4: High resolution images of Class II MHC and CD68 staining for in-flammatory cells. Both slides are from Case 3, 9mm rostral to the epicentre.5.2 Measuring MRI metric sensitivity to myelin andinflammationOur aim was to investigate the correlation between the histologic distribution of myelinloss and inflammatory cells in the injured spinal cord and a number of myelin- andinflammation-sensitive MRI measures: MWF, ihMTR, DTI, and DKI.Many previous in vivo MRI studies in human traumatic SCI have sought to estab-lish biomarkers of this kind by correlating conventional and advanced imaging mea-sures with neurologic impairment, such as the ISNCSCI [148], and eventual outcome.[162, 233–247] However, to our knowledge, no prior studies have validated advancedMRI biomarkers of human SCI by directly comparing MRI measures of the cord withhistologic staining of the same tissue.Our principal aim was to identify possible myelin biomarkers in human SCI. Wealso had three secondary aims:1. To investigate whether the post-mortem, fixation and injury-death interval affectedhistological staining and qMRI metrics.2. To investigate how each of the MRI metrics change in the presence of inflam-136mation. The effect of inflammatory cells on MRI measures of myelin in humantraumatic SCI has not yet been examined.3. To measure whether inflammatory cells and myelin loss were co-localised acrossspinal cord cross-sections.Empirically establishing the relationship between qMRI metrics and the underlying tis-sue microstructure will pave the way to in vivo MRI insights into the pathologic pro-cesses occurring within the injured cord over time.5.2.1 MethodsSix patients from the ISCIB: 4 male, 2 female, mean age 80 yrs, range 60-95 yrs, wereincluded in this study. Figure 5.1 describes all eight patients in the ISCIB including theirinjury level, severity, post-mortem interval, injury-death interval and primary diagnosis.However in this study, Cases 1 and 6 were excluded due to issues with the MRI and/orhistology data.MWI multi-echo T2 decay data were fit using a NNLS algorithm with flip angleoptimization and stimulated echo correction to produce T2 distributions. [30, 164] Athreshold of T2 = 24 ms was applied to generate MWF maps of the fraction of the signalin each voxel below this threshold. A lower T2 threshold is required than the T2 = 40ms used in vivo because formalin fixation reduces T2 in tissue. [153, 248] ihMT imageswere combined using Equation 1.21 in MatLab.Five white matter and two grey matter regions were manually drawn on each dig-itized histology slide and corresponding MRI slice independently in 3D Slicer. [249]Blood vessels, other areas of confounding staining, and any small rips/holes in the tis-sue were carefully excluded (Figure 5.5). Two sections were quantified, one 9mm rostraland one 9mm caudal to the injury epicentre. The 9mm distance was measured carefully,137Figure 5.5: An example manual region of interest segmentation of the histologydata, carefully excluding tissue rips and blood vessels.however the method for designating the epicentre was based on the external appear-ance of the cord and was fairly approximate, and so these slices may not be centredon the true epicentre. Saturation images were produced from the raw histology scans,combining the red, green and blue channels. These saturation images were thresholdedusing an Otsu algorithm to produce binary images labelled for positive myelin (LFB)and activated microglia and macrophage (Class II MHC and CD68) staining.The Otsu method, named after Nobuyuki Otsu who first implemented it in 1979, isa simple way to perform automatic image thresholding by choosing a threshold value(T ) which minimises the intra-class intensity variance. [250] The weighted intra-classthreshold, σ2(T ), is minimised, whereσ2(T ) = ω1(T )σ21 (T )+ω2(T )σ22 (T ) (5.1)Here ωi(T ) is the probability weighting of class i separated by the threshold T , andσ2i (T ) is the intra-class variance for class i.The stained area fraction for each region was calculated. The mean MRI metric wasread out for each region. Pearson correlation coefficients (ρ) and p-values were calcu-138lated between MRI and histology mean region values. The significance threshold wasset using a strict Bonferroni correction for multiple comparisons, of which we performed204: ∗ : p < 0.05/204,∗∗ : p < 0.01/204,∗∗∗ : p < 0.001/204.5.2.2 ResultsFigure 5.6 displays representative anatomical, myelin-sensitive and DTI/DKI MRI met-ric maps from the six traumatic SCI cases included in this study. Qualitative observationshowed clear areas of greater and lesser tissue damage in the anatomical images. T1D fil-tering reduced the total ihMTR signal and also increased the white/grey matter contrast.MWF maps contained more noise than the ihMTR maps and the DKI maps containedmore noise than the DTI maps.Case 8, which had the longest injury-death interval, showed a large decrease in DKIand DTI FA across both white and grey matter compared to the other cases in which theaxons had less time to degenerate. Case 8 also had lower ihMTR than the other cases,particularly rostral to the epicentre. The caudal slice of Case 8 was very distorted due tochanges in morphology from the injury and tissue degeneration during the injury-deathinterval. Qualitatively comparing the maps with the patients’ known injury severities,we saw areas of higher DTI FA in Cases 4, 5 and 7 (AIS C, B and D, see Section 2.5)which might indicate spared axons contributing to the retention of motor/sensory func-tion below the injury. Case 3 (AIS A) also had high FA rostral to the injury, but this wasnot also seen in the caudal slice.Figure 5.7 displays representative histological staining images from the six trau-matic SCI cases included in this study. Visual observation revealed a distinct increase inCD68 staining with time after injury, which is expected for the timeline of macrophageinfiltration. Class II MHC also increased gradually over the range of injury-death inter-vals. Rating the pathological tissue based on the appearance of tissue damage including139Figure 5.6: MRI metric maps from the six patients included in this study. The two slices we used from each case are shown,9 mm above and 9mm below the injury epicentre. (Note: missing images represent cases which had acquisition issuesfor the specific scans and so were not included in the analysis.)140myelin loss, axon degeneration and axon swelling showed results clearly related to theinjury severity. Cases 2, 3 and 8 (all AIS A) had the most damage seen in the Hema-toxylin and Eosin stain (H&E), which is used to visualise the overall cell distributionand tissue structure, in slides spanning the epicentre, affecting the entire cross sectionof the cord for at least 3 consecutive slides (9 mm in length, slides not shown in thisthesis). Cases 4, 5, 7 (AIS C, B, D) did not show involvement of the entire spinal cordcross section. In particular, Case 7 (AIS D) showed minimal histological changes.Correlations with injury-death, post-mortem and fixation intervalInjury-death, post-mortem and fixation interval, in days, was correlated with each ofthe metrics. CD68 was the only stain which showed a strong correlation with injury-death interval (Figure 5.8). I/EW gmT2 and all of the diffusivity metrics correlatedpositively with injury-death interval. ihMTRf and ihMTRuf, FA and all of the DKImetrics correlated negatively with injury-death interval. Post-mortem interval correlatedpositively with both of the stains for inflammatory cells, I/EW gmT2 and DKI RD andMD, and negatively with DTI FA. Fixation interval correlated positively with both ofthe inflammatory cell stains, I/EW gmT2, and DTI and DKI RD and MD. Fixation timecorrelated negatively with ihMTRf and ihMTRuf, DTI and DKI FA and DKI RadialKurtosis (RK) and Mean Kurtosis (MK).Correlations with myelinationLFB myelin staining area fraction was significantly correlated with ihMTRf and ihMTRuf,MWF and I/EW gmT2 (Figure 5.9). ihMTR had the strongest correlation, followedby the MWI metrics. T1D-filtering very slightly decreased the LFB-ihMTR correlationstrength and moved the intercept closer to zero. LFB staining correlated with DTI FAbut was not correlated with any of the other DTI or DKI metrics.141Figure 5.7: Representative histological staining images from the six patients included in this study. CD68 increased withincreasing injury-death interval, which is demonstrated by higher resolution images: (A) an area of low CD68 stainingfraction from Case 2 and (B) an area of high CD68 staining fraction from Case 8.142Figure 5.8: Heatmaps showing the Pearson correlation strength (r) across everyvariable in the dataset. Stars give significance level after strict Bonferronicorrection for multiple comparisons. (number of comparisons = 204, ∗ : p <0.05/204,∗∗ : p < 0.01/204,∗∗∗ : p < 0.001/204).143Figure 5.9: All fifteen MRI metrics correlated with LFB stain area fraction for myelin lipids. Correlation lines and Pearsoncoefficients are only shown for correlations with p < 0.001. Grey matter is denoted by open circles, white matter byfilled circles.144Correlations with inflammationihMTRuf and DTI and DKI FA were significantly negatively correlated with Class IIMHC staining, while DKI RD was significantly positively correlated (Figure 5.10, Fig-ure 5.11). These correlations, except with ihMTRuf, were also seen with CD68. CD68also had a positive correlation with DTI RD, DKI AD and DKI MD. Neither of theinflammatory cell stains were correlated with any of the DKI kurtosis metrics.Correlations between MRI metricsihMTRf and ihMTRuf were correlated with many of the DTI/DKI metrics (positive cor-relation: DTI FA, DKI FA, DKI RK, negative correlation: DTI RD, DKI RD, DKI MD)and was negatively correlated with I/EW gmT2 (Figure 5.8). ihMTRf was additionallypositively correlated with DKI MK and negatively correlated with DTI MD and DKIAD. Finally, IE/W gmT2 was positively correlated with RD and MD. There were nosignificant correlations between MWF and any other metric.Correlations between histology stainsThere was no correlation between LFB and either of the inflammatory cell metrics. ClassII MHC and CD68 had a significant positive correlation (Figure 5.12).5.2.3 DiscussionIn this study we had a unique opportunity to examine the relationship between MRIbiomarkers and histological staining for myelin and inflammation in six individuals withtraumatic SCI. Our main aim was to establish imaging biomarkers for myelin loss inthis tissue. We found that both ihMTR and MWF were good biomarkers for myelin intraumatic SCI tissue. ihMTRf had the greatest myelin specificity. DTI FA correlatedwith myelin staining but none of the DKI metrics were significantly sensitive to myelin145Figure 5.10: DTI/DKI metrics correlated with Class II MHC and CD68 stain area fraction. Correlation lines and Pearsoncoefficients are only shown for correlations with p < 0.001. Grey matter is denoted by open circles, white matter byfilled circles.146Figure 5.11: Myelin-sensitive MRI metrics correlated with Class II MHC andCD68 stain area fraction. Correlation lines and Pearson coefficients areonly shown for correlations with p < 0.001. Grey matter is denoted by opencircles, white matter by filled circles.147Figure 5.12: Correlations between the histology stain area fractions: Class IIMHC, CD68, and LFB. Grey matter is denoted by open circles, white mat-ter by filled circlesin traumatic SCI tissue.Our secondary objective was to investigate how these biomarkers might be affectedby the presence of inflammation in the spinal cord. ihMTRuf and DTI/DKI FA were de-creased, while DKI RD was increased, in areas of inflammation. MWF was not affectedby inflammation. To determine if decreased myelin in areas of inflammation was driv-ing decreases in ihMTRuf, we compared the myelin and inflammation stains and weresurprised to find that there was no correlation between LFB and either Class II MHC orCD68.148Correlations with injury-death, post-mortem and fixation intervalOne key aspect of this study is the wide range of injury-death interval across the sixpatients (9−112 days) meaning each case was at a different stage of the traumatic SCIsecondary injury timeline described in Section 2.5. ihMTR, FA and all of the DKImetrics decreased with injury-death interval, as the tissue around the primary injurysite experienced inflammation and axon loss. The decrease in kurtosis was expected asthis had previously been seen in more damaged areas of MS lesions in both grey andwhite matter. [251, 252] The increase in I/EW gmT2 with injury-death interval maybe due to tissue loss resulting in “freer” relaxation. AD, RD and MD increased withinjury-death interval as might be expected due to axon loss and degradation of the whiteand grey matter tissue structure. However, we did not investigate axon damage or losshistologically in this study. CD68 was the only stain which showed a correlation withinjury-death interval; we found that it increased greatly and was almost doubled in Case8 with the longest injury-death interval compared to all of the other cases, which canclearly be seen in Figure 5.7. On the other hand, Case 5 had a noticeably greater CD68area fraction than Case 4, even though their IDIs were similar (17 vs. 15 days) and soit is likely other factors including age, sex and injury severity are also influencing CD68staining area.Previous studies have found that an increased post-mortem interval between deathand fixation of the human brain decreased DTI FA and increased DTI AD, RD and MD.[253] We found very similar results, although some of the correlations did not reachsignificance. We measured an increase in I/EW gmT2 with post-mortem interval. Asnervous tissue is fixed, water is removed from the intra-extracellular space, [254] whichcan increase the MWF. [255] However experiments in rat spinal cord found that the maindriving effect of the MWF increase was an increase in myelin water, not a decrease149in intra/extracellular water. [256] Further investigation is needed to determine whichtissue processes are occurring to increase the I/EW gmT2 when fixation is delayed. Theincrease in I/EW gmT2 could perhaps be related to post-mortem autolysis, which resultsin increasing breakdown of cell membranes over time and thus more exchange betweenthe intracellular and extracellular spaces. The correlation between inflammatory cellstaining and post-mortem interval is driven by the fact that Case 8 had the highest post-mortem interval and also the highest inflammatory cell staining. A larger study includingmore subjects from the ISCIB is needed to disentangle these effects.Correlations with myelinationBoth MWF and ihMTR correlated with LFB staining, demonstrating that both tech-niques are sensitive to myelin in ex-vivo human spinal cord. T1D-filtering very slightlyweakened the ihMTR-LFB correlation (from r = 0.65 to r = 0.63), perhaps due to thelower SNR acquired using this saturation scheme reducing myelin sensitivity. T1D-filtering also moved the intercept closer to zero indicating increased myelin specificity.[66]An important question in the field of myelin imaging is to what extent the qMRItechniques detect myelin debris as well as intact myelin. Research in rat SCI has previ-ously demonstrated that MWF is sensitive to myelin debris quantified by immunohisto-chemistry of myelin basic protein at 3 weeks post-injury. [257] LFB will stain myelinlipids regardless of whether they are part of an intact myelin sheath, and so a strongcorrelation with LFB across areas that are likely to include myelin debris could indicatethat signal being captured by the qMRI metric includes myelin debris.Only DTI FA correlated with LFB, perhaps because FA is a very general markerof tissue integrity which is sensitive to myelin integrity as well as other factors such asedema and axon alignment. None of the other diffusion metrics correlated with LFB150and we did not observe a correlation between RD and myelin staining, in contrast toprevious studies which have found a relationship between increases in RD and demyeli-nation, particularly in MS. [156, 258, 259] However, some researchers have questionedthe reliability of drawing a direct link between RD and myelin loss, particularly in tis-sue where there are complex multicellular processes occurring, such as remyelinationand disruption of the white matter resulting in crossing fibres. [101, 260] Furthermore,demyelination in MS is a very different process to myelin loss after trauma. In theformer, axons stay relatively intact, whereas in the latter, they may be completely de-stroyed. This could change the myelin loss/RD relationship, removing the correlationwhich is often seen. None of the kurtosis metrics were correlated with LFB suggestingthat myelin loss is not affecting diffusion kurtosis.Correlations with inflammationThe unexpected correlation between Class II MHC/CD68 and ihMTRuf suggests thatthere is a link between inflammation and ihMT-derived metrics in the injured spinalcord. This is an important effect to quantify, given that inflammation is so widespreadafter traumatic SCI, and many potential interventions aim to modulate post-traumaticinflammation as a therapeutic strategy. Of course, we acknowledge that this strong cor-relation between higher inflammatory cells and lower ihMTRuf in more damaged areas,does not necessarily prove that inflammation is causing myelin loss. The fact that therewas no correlation between LFB and Class II MHC or CD68 would support the in-terpretation that the ihMTRuf changes are reflecting sequelae of inflammation, such asedema, axon changes etc, rather than loss of myelin lipids. Furthermore, MWF was notcorrelated with the presence of inflammatory cells, which further suggests that myelindecreases are not what is affecting ihMTRuf in the areas of inflammation. This resultis perhaps a little surprising, as inflammation is often associated with edema which can151cause an artificial decrease in MWF by increasing the denominator of the fraction de-picted in Figure 1.10. We did not histologically assess edema in this study and so wecannot be sure whether inflammatory cells and edema are co-localised in this tissue. Itis conceivable that ihMTRuf is detecting changes in myelin occurring in areas of inflam-mation which do not yet manifest in a reduction of the LFB staining area. Performingpartial correlation analysis on the CD68/ihMTRuf correlation showed a small increasein the correlation strength when controlling for LFB (r = -0.43 to r = -0.60), due to thefact that these two stains have opposite effects on the ihMTR.The strong correlation between FA/RD and Class II MHC/CD68, shows that DTImetrics are greatly affected by the presence of inflammatory cells. The strong negativecorrelation between FA and both Class II MHC and CD68 was largely driven by anincrease in RD. Particularly in Case 8, with the longest injury-death interval, areas ofhigh CD68 staining correlated with large increases in RD and decreases in FA. RD isknown to be influenced by processes occurring with inflammation such as axon swellingand myelin degradation, [157] which decrease the barriers to diffusion perpendicularto the white matter tracts. Our results could suggest that FA and RD may be goodmarkers for inflammation after traumatic SCI, however the interpretation of DTI metricsin the acute post-injury setting may be limited because they are influenced, not just bythe injury itself, but the inflammatory cellular response to injury. One could insteadinterpret DTI changes in an acute setting as non-specific indications of tissue pathology.No changes in kurtosis were observed in areas of inflammation.Correlations between MRI metricsThe significant correlations between most DTI metrics was expected; the DTI/DKI equa-tion is fit to the diffusion signal in each voxel to provide all relevant metrics and theyare not independent. ihMTRf and ihMTRuf were correlated with many of the DTI/DKI152metrics (positive correlation: DTI FA, DKI FA, DKI RK, negative correlation: DTI RD,DKI RD, DKI MD) and negatively correlated with I/EW gmT2. These correlations con-nect to the tissue health in general; ihMTR, FA and RK are low in areas of damage wherethe diffusivity (affecting RD, MD and I/EW gmT2) is higher due to tissue degradation.This general picture is supported by the positive correlation between I/EW gmT2 andRD and MD. The fact that there was no correlation between MWF and any other metric,when we would expect to see a correlation with at least ihMTR, may be due to the lowSNR of the MWF maps (Figure 5.6). It may also show that MWF provides differentinformation to the other MRI metrics investigated.LimitationsThis study had a few limitations which are important to note, the main limitation beingthe small sample size. Data from only six patients was analysed and all of these patientswere over 60 years old, reflecting the fact that mortality is high in more elderly patientsafter suffering such a significant physiologic insult. People over the age of 60 representa rapidly increasing demographic for traumatic SCI, and so the insights from our workwhich involved primarily elderly patients are certainly applicable to this demographic.We acknowledge that there may be MRI and histological differences in younger patients.We are also comparing data from patients with a wide range of post-mortem andinjury-death intervals, at different stages of secondary injury after the initial impact.However, despite this, a strong relationship was noted between histology and MRI met-rics.The MRI metrics and staining fractions were averaged over fairly large atlas-definedregions in order to mitigate any small differences in the edges of the manually delineatedregions, which will have reduced our sensitivity to small changes in both histology andMRI metrics occurring within each region.153The inflammatory cell correlation was performed using staining area fractions, how-ever it would be interesting to try the same analysis quantifying the histology using acell counting method. Some of the macrophages are very large and so contribute ap-preciably to the staining area fraction but would only be counted as one cell. Changesto the correlations could help to disentangle the effects of the macrophages/microgliathemselves on the qMRI metric, compared to the accompanying inflammatory milieu.Correlations were performed over all of the regions in this study, white and greymatter combined, however when the ihMT, MWI, DTI and DKI metrics were correlatedwith Class II MHC and CD68 in Figure 5.11 and Figure 5.10 it appears that there isa separate correlation line for the grey matter regions for some of the metrics. Thisseparation occurs because the majority of the MRI metrics measure a difference betweengrey and white matter regions while the Class II MHC and CD68 staining does not.Translating qMRI techniques to clinical use for SCIThis analysis was performed on a 7 Tesla scanner. While human 7 Tesla scanners arebeginning to be available in research-settings it is unlikely they will be used in a clinicalcontext. However, all three of the MRI techniques used in this study are available in vivoon 1.5 Tesla and 3 Tesla scanners. More research is needed to translate these techniquesinto common clinical use and a key component of that will be investigating the effectof different field strengths on the myelin specificity of these scans. We anticipate that alower field strength will not affect the utility of ihMT, MWI, DTI and DKI to producethe biomarkers discussed.One aspect of in vivo clinical imaging which will affect the scans produced is thelower resolution available in vivo, which determines the scale of the changes that willbe visible. However, the regions of interest in this study were quite large, much largerthan pixel sizes available in the spinal cord in vivo, and we still saw variation in MRI154metric values between them.All of these imaging techniques would be greatly affected by the presence of metalspinal fixation implants placed adjacent to the spinal cord during surgery, which willcause MRI artifacts. When implementing these scans in vivo one would either have toimage prior to surgery (three of the seven MRI scans seen in Figure 5.1 were obtainedbefore surgery) or choose a FoV away from the implant, for example for a thoracicinjury we could scan the cervical cord, which could give useful information on the typesof degeneration and inflammation which occur proximal and distal to the injury.5.2.4 ConclusionThe overall aim of this study is to investigate biomarkers which could be used in vivoto better visualize the location and extent of the primary and secondary injury whichoccurs after traumatic SCI. We validated two myelin biomarkers (ihMTR and MWF)and found three MRI metrics affected by inflammation (ihMTRuf, DTI FA, RD) whichcould be used, perhaps in combination, to create a map of the microstructural changesoccurring after traumatic SCI. Both myelin metrics investigated, MWF and ihMTR, hadsignificant correlations with LFB staining for myelin lipids, supporting the use of bothas biomarkers for myelin loss after traumatic SCI. ihMTRuf decreased in areas of in-flammation, revealing a link between inflammation and this metric in the injured spinalcord. FA and RD correlated strongly with Class II MHC and CD68 and so may beuseful biomarkers for inflammation after SCI, however linking these metrics to specificbiological processes is difficult as many aspects of tissue microstructure near the injurywill affect the diffusion of water. Further studies are warranted to investigate the rela-tionships between the many processes which affect MRI biomarkers in SCI tissue. Thiswork is an important first step towards using these techniques to see inside the injuredspinal cord in vivo to aid clinical decision-making and therapeutic interventions.155Figure 5.13: 7 Tesla high-resolution post-mortem anatomical scans for each Casein this study. Three slices are shown: one at injury epicentre, one 4.5 cmabove and one 4.5 cm below.5.3 Tracking MRI metric changes along the cordWe were interested in the distribution, extent, and timeline of the different types of mi-crostructural damage which propagate out from the epicentre after a spinal cord injury,and how 7 Tesla quantitative MRI techniques might be able to measure this damage.Previous studies have detected both retrograde and Wallerian degeneration using MRIin vivo (see Section 2.5.1). [150, 151] Our objective was to evaluate whether theseadvanced qMRI metrics could also detect retrograde and Wallerian degeneration post-mortem, and utilise the wide range of acute and sub-acute injury-death intervals seen inthe ISCIB to assess the time-course of MRI-detected degeneration using post-mortemhuman spinal cord tissue.5.3.1 MethodsFor this study, Cases 2 and 3 in Figure 5.1 were not included, as the patients’ data hadnot been collected yet. Figure 5.1 describes the six cases in this study including theirinjury level, severity, post-mortem interval, injury-death interval and primary diagnosis.For each of the six spinal cords included in this study, three 4.5 cm long pieces ofspinal cord were scanned and analysed: one centred on the injury epicentre (determined156during autopsy), one above (the rostral segment) and one below (the caudal segment).13.5 cm of cord MRI data in total was quantified for each cord. Except for the ihMTRinvmaps, for which no data was collected for the central epicentre section (see Figure 5.16).Anatomical images were manually segmented into white matter and grey mattermasks in 3D Slicer. [249] Each 4.5 cm section of spinal cord tissue was assigned twoatlas levels from the histological spinal cord atlas published by Sengul et al. [9], usingthe known level of the injury, and the appearance of the spinal cord morphology. Thisis necessary as the morphology of the white and grey matter of the cord changes signif-icantly along its length. These atlas cross sections were then registered slice-wise to theanatomical MRI scans using a Coherent Point Drift algorithm implemented in MatLab.[261] Metric values were examined in five white matter areas, shown in Figure 2.5. Themetric value from each of the five white matter tracts for every slice of the MRI dataacross 13.5 cm of the spinal cord spanning the injury epicentre was extracted.Six slices (each 1mm in width) from both ends of each cord section were excludedfrom the maps, due to signal drop-out near the ends of the receiver coil. This is whatcauses the gaps in the data seen in Figure 5.14, Figure 5.15 and Figure 5.16. Each pointin these graphs represents the metric value averaged across all voxels in that slice in oneregion of interest.5.3.2 ResultsDTI along the cordExcluding Case 7, FA decreased with longer injury-death interval (Figure 5.14). FA wassharply decreased at the epicentre in the three cords with the most severe injuries (A)(Cases 1, 6, 8). A distal decrease in FA was seen in the two cords with the longest injury-death interval (Cases 7, 8), with lower FA in descending tracts below the injury and157ascending tracts above.1 MD was much higher in Case 8. It can be seen in Figure 5.13,the caudal section of Case 8 is distorted; this part of the cord had lost tissue integrityduring the long injury-death interval and so the cord was slightly crushed by the cuttingprocess.DBSI along the cordFiber fraction was increased in Cases 1 and 4, the two cords with shortest injury-deathinterval (Figure 5.15). Restricted fraction was highest in Cases 5, 6 and 7. Hinderedfraction was much higher in Case 8, suggesting the increased DTI MD corresponds tothe extracellular DBSI component. In Cases 7 and 8 the descending tracts had slightlyhigher restricted fraction below the injury, while the ascending tracts had higher re-stricted fraction above the injury, the same areas where we saw lower fiber fraction.This makes sense as we would expect to see a high concentration of microglia andinflammatory cells removing axon and myelin debris in areas experiencing Walleriandegeneration.ihMTRinv along the cordWhen we first qualitatively examined the data we saw that in Cases 7 and 8 there wasa very clear Wallerian degeneration pattern in the ihMTRinv maps (Figure 5.17). In thedorsal column, which is mainly composed of ascending sensory tracts, the ihMTRinv waslower above the injury, whereas in the lateral corticospinal descending motor tracts, theihMTRinv was much lower below the injury. The cuneate fasciculus does also containsome descending motor tracts and some brightness can be seen, particularly in Case 8.Figure 5.16 shows the decrease due to Wallerian degeneration quantitatively.1“proximal” and “distal” of the injury are defined with respect to the direction of the nerve impulsestravelling along the axons. So for example above the injury would be “distal” for ascending sensory tracts,but “proximal” for descending motor tracts.158Figure 5.14: DTI metrics for each patient in each white matter tract. AIS severitygrade is shown in brackets. Metric values are shown for ∼13.5 cm of thecord extending from +67.5 mm (rostral to the injury) to -67.5 mm (caudal tothe injury). Black arrows indicate trends in FA above and below the injuryepicentre associated with Wallerian degeneration.159Figure 5.15: DBSI metrics for each patient in each white matter tract. AIS severitygrade is shown in brackets. Metric values are shown for ∼13.5 cm of thecord extending from +67.5 mm (rostral to the injury) to -67.5 mm (caudalto the injury). Black arrows indicate trends in fibre and restricted fractionsassociated with Wallerian degeneration.160Figure 5.16: ihMTRinv for each patient in each white matter tract. Metric valuesare shown for∼13.5 cm of the cord extending from +67.5 mm (rostral to theinjury) to -67.5 mm (caudal to the injury). Full ihMT data was not acquiredfor the central epicentre block, or for Case 4.Figure 5.17: Representative slices of the axial ihMTRinv maps from above and be-low the epicentre in Cases 7 and 8, which had the longest injury-death inter-val. Clear decreases in signal related to Wallerian degeneration are seen thegracile and cuneate fasciculus above the injury and the lateral corticospinaltracts below.1615.3.3 DiscussionIn this study we had a unique opportunity to conduct post-mortem imaging of humanspinal cords after traumatic SCI. We found that, as we expected, cords with a shorterinjury-death interval had higher FA and fiber fraction, as there was less time for axonsto degenerate and increase the isotropy of diffusion. The sharp decrease in FA at theinjury epicentre in cases with a complete injury is consistent with severe focal cordwhite matter damage and complete loss of tissue integrity, which can also be seen in theanatomical images in Figure 5.13. These cords were difficult to section at the epicentrebecause the formalin-fixed cord would be crushed by even a sharp razor blade.The lower fiber fraction and FA we measured in distal areas for Cases 7 and 8 maybe attributed to decreased white matter integrity and axonal loss due to Wallerian de-generation. This result is corroborated by previous in vivo DTI findings in chronic SCI.[151] Distal decreases in ihMTRinv in Cases 7 and 8 provides further evidence of whitematter damage and myelin loss. Our results may give a time interval for measuring slowdemyelination and axon degeneration with ihMT and diffusion techniques.5.3.4 ConclusionA sharp FA and DBSI fiber fraction decrease was seen at the injury epicentre in patientswith no motor/sensory function below the injury, which was not observed in patientswith less severe injuries. We found evidence of Wallerian degeneration specifically inthe two cords with injury-death interval greater than 35 days. In distal areas, therewas lower DBSI fibre fraction, DTI FA, ihMTRinv and higher DBSI restricted fraction,reflecting axon loss, myelin loss and inflammation respectively. These results give usan approximate timeframe in which we can detect Wallerian degeneration using theseadvanced qMRI techniques.1625.4 Future researchThe ISCIB data set is an invaluable resource as it includes both advanced qMRI and his-tological staining for matched slices throughout the cord. There are so many interestingquestions which could be asked of this data, both about the biology of traumatic SCI andabout qMRI in the context of seriously injured central nervous system tissue. The twostudies presented in this thesis are only the very beginning of what can be done with thisdata set. There are many further ideas which could be explored.This is a very large data set and as such, a large part of utilising it is having ef-ficient, automated analysis methods and tools. In this Chapter, some of the methodsI developed I believe are good options, for example quantification of histology stain-ing area fractions in Section 4.1, but others were manual and sometimes unreliable. Inparticular, drawing regions of interest on the histology data had to be done manuallyto exclude rips and blood vessels which would have biased the results, however if thiscould be automated, then the many hundreds of stained histology slides in the ISCIBcould be quickly and easily compared. Also, the slice-by-slice Coherent Point Driftregistration used in Section 4.2 was acceptable but not always extremely accurate andrequired manual grey/white matter segmentation of the anatomical MRI data first. Im-proving the registration, segmentation and quantification of the ISCIB data will be animportant project for another graduate student.An interesting area to explore is comparing the qMRI metric maps of different tis-sue microstructural parameters to clinical information including injury mechanism andneurological assessments, to see if the injury mechanism is reflected in the distributionof damage, and whether structural-functional links can be made between MRI-measuredtissue damage and resulting neurological deficits.A lot of the limitations mentioned in Section 5.2.3, such as the small sample size163and the variation in parameters including injury mechanism, injury severity, injury-deathinterval, post-mortem interval and age could be fixed using an animal model of spinalcord injury. This would allow for a more controlled validation of the sensitivity andspecificity of MRI markers of tissue damage.The high angular resolution multi-shell diffusion data collected for the ISCIB couldbe analysed/modelled in many different ways. In this thesis, I explored DTI, DKI andDBSI. I did not choose to use some of the other diffusion models (e.g. NODDI) as Iwas concerned that the assumptions of, for example axial diffusivity in the anisotropiccompartment, might not be appropriate in the context of the highly disrupted spinal cordmicrostructure seen after traumatic SCI. An interesting research project could involveinvestigating the results from these models, including DBSI, in more detail to validatethe tissue model parameters used in this context.In Section 5.2 I analysed correlations with different histological staining. An in-teresting direction might be to combine staining area fraction from different stains formyelin, axons, inflammatory cells etc. to create a multivariate regression model for themicrostructural contributions to the qMRI signal.164Chapter 6ConclusionsIn this thesis, four qMRI metrics sensitive to myelin are investigated. MWI, whichseparates the signal from water trapped between myelin bilayers by its short T2 time.MT which uses an off-resonance pre-pulse to create a contrast sensitive to the “invisible”macromolecular protons via magnetisation transfer. ihMT, which uses five images withdifferent off-resonance pre-pulses to produce an image weighted by the dipolar orderin the tissue. DWI, which can track the diffusion of water molecules over micrometrelength scales and so is sensitive to changes in the tissue microstructure. The overarchingaim of this research is to optimise, validate and apply these techniques so that they canprovide more accurate and detailed information about the presence or absence of myelin.The clinical use of these techniques could facilitate large improvements to prognosis andpatient care.In Chapter 3, I compared MT, ihMT and MWI in healthy adult brains and MWIand DTI in pediatric brains. Characterising these metrics in the healthy brain is impor-tant before they can be applied to the study of diseases. The atlases of myelin contentI created in this Chapter have been made available to the public and may be used tocreate Z-score maps for individual patients. In this Chapter I also investigated the repro-165ducibility of MT, ihMT and MWI which is an important metric to document as it willdetermine the study size needed to accurately detect effects of a particular size at a givensignificance threshold.In Chapter 4, I investigated the effect of white matter fibre orientation with respectto B0 on MT, ihMT and MWI. This is an interesting area which has only recently be-come the focus of qMRI research. My results suggest that microstructural parametersother than the fibre orientation, such as myelination, axon diameter or crossing fibresare affecting the apparent orientation dependence curves measured in different regionsof the brain. The technique generally used to measure orientation dependence, whichinvolves averaging the qMRI metric in question across all white matter voxels with thesame fibre angle, may be producing misleading apparent orientation dependence trends.I hope that this paper will encourage researchers to use other techniques to track theorientation dependence including rotating the white matter fibres in the scanner either invivo or ex vivo and modelling the effect of microstructural parameters on the orientationdependence. I also present the results of an in vitro study to determine the contribut-ing factors to the orientation dependence of ihMTR. We found that the lipid linewidthappeared to have a greater effect on the orientation dependence of ihMTR than R1D al-though both quantities had a significant orientation dependence. This research raises aninteresting question about the effect of the chosen off-resonance frequency on the orien-tation dependence of ihMTR. We hypothesise that increasing the offset frequency mayincrease the contribution of the lipid linewidth to the orientation dependence of ihMT.Future experiments are needed to test this hypothesis.In Chapter 5 I applied ihMT, MWI and three diffusion analysis methods: DTI, DKI,DBSI, to the study of human traumatic spinal cord injury tissue from the ISCIB. InSection 5.2, I used histological staining for myelin lipids and inflammatory cells to in-vestigate the correlations between the qMRI metrics and these tissue components. We166found that both ihMTR and MWF were correlated with staining for myelin lipids and sothese techniques are very promising biomarkers for myelin loss after spinal cord injury.The next steps in bringing these qMRI techniques to clinical use will be to optimisethem for use in the spinal cord in vivo in the presence of metal SCI cord stabilisationhardware. Finally, in Section 4.2, I present the results of an analysis of qMRI maps infive white matter regions along 14 cm of the spinal cord, centred on the injury epicentre.In this study we were interested in whether the qMRI techniques could detect Wallerianand/or retrograde degeneration, as knowledge of the severity and extent of secondaryinjury in the cord would be very valuable for clinical trials and the application of treat-ments such as electrical stimulation and stem cell therapies. We found that Walleriandegeneration was detected in the two Cases with the longest injury-death intervals (60and 112 days). Future research using the data from more biobank Cases will providemore detail on the detection of secondary injury using qMRI.This thesis summarises my research into qMRI techniques sensitive to myelin. 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Thesis/Dissertation
eng
Physics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Laule, Cornelia
Magnetic resonance imaging to measure myelin : orientation dependence and application to spinal cord injury
Text