Western University Scholarship@Western Electronic Thesis and Dissertation Repository April 2013 Magnetic resonance imaging of brain tissue abnormalities: transverse relaxation time in autism and Tourette syndrome and development of a novel whole-brain myelin mapping technique Yann Gagnon The University of Western Ontario Supervisor Drs. Jean Théberge and Rob Nicolson The University of Western Ontario Graduate Program in Medical Biophysics A thesis submitted in partial fulfillment of the requirements for the degree in Doctor of Philosophy © Yann Gagnon 2013 Follow this and additional works at:http://ir.lib.uwo.ca/etd Part of theDevelopmental Neuroscience Commons,Medical Biophysics Commons,Mental Disorders Commons, and theNeurosciences Commons Recommended Citation Gagnon, Yann, "Magnetic resonance imaging of brain tissue abnormalities: transverse relaxation time in autism and Tourette syndrome and development of a novel whole-brain myelin mapping technique" (2013).Electronic Thesis and Dissertation Repository. Paper 1189. This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please [email protected]. Magnetic resonance imaging of brain tissue abnormalities: transverse relaxation time in autism and Tourette syndrome and development of a novel whole-brain myelin mapping technique (Thesis format: Monograph) by Yann S. Gagnon Faculty of Medicine and Dentistry Department of Medical Biophysics Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy The School of Graduate and Postdoctoral Studies The University of Western Ontario London, Ontario, Canada March 28, 2013 (cid:13) c Yann S. Gagnon, 2013 Abstract The transverse relaxation time (T2) is a fundamental parameter of magnetic res- onance imaging sensitive to tissue microstructure and its water content, thus offering a non-invasive approach to evaluate abnormalities of brain tissue in-vivo. Prevailing hypotheses of two childhood psychiatric disorders were tested using quantitative T2 imaging and automated region of interest analyses. In autism, the under-connectivity theory, which proposes aberrant connectivity within white matter (WM), was as- sessed, finding T2 to be elevated in the WM of the frontal and parietal lobes, while dividing whole brain data into neurodevelopmentally relevant WM compartments found increased T2 in bridging and radiate WM. In Tourette syndrome, tissue ab- normalities of deep gray matter structures implicated in the symptomology of this disorder were evaluated and increased T2 of the caudate was found. Despite the sensitivity of quantitative T2 measurements to underlying pathophys- iology, interpretation remain difficult. However, in WM, the compartmentalization of distinct water environments may lead to the detection of multi-exponential T2 decay. The metric of interest is principally the myelin water fraction (MWF), which is the proportion of the T2 signal arising from water trapped within layers of the myelin sheath. As a proof of concept study, the ability to measure the MWF based on T2* decay was evaluated and compared to a MWF measurements obtained from T2 decay. Data were analysed using both non-negative least squares and a two-pool model. Signal losses near sources of magnetic field inhomogeneity, such as the sinuses, rendered T2* components inseparable, invalidating this approach for whole brain MWF mea- surements. However, this study demonstrated the suitability of a two-pool model to calculate the MWF in WM. ii A novel approach, based on the multi-component gradient echo sampling of spin echoes (mcGESSE) and a two-pool model of WM, is proposed and its feasibility demonstrated using simulations. The in-vivo implementation of mcGESSE followed, with reproducibility of MWF measurements being assessed and the potential of an accelerated protocol using parallel imaging being investigated. While further work is needed to assess data quality, this approach shows great potential to obtain whole brain MWF data within a clinically relevant scan time. Keywords: autism, Tourette Syndrome, under-connectivity, white matter, trans- verse relaxation, T2, multi-component, myelin water fraction iii Co-Authorship This thesis contains material from manuscripts either submitted or in preparation for submission. Forthestudiesofchapter2investigatingtransverserelaxationtimesinautismand Tourette syndrome, Drs. Nicolson and Williamson completed the psychiatric evalu- ations and subject recruitment; Ms. Hendry and Drs. DeVito, Drost and Gelman designed the MRI acquisition protocol; Drs. DeVito, Drost, Gelman and Nicolson as well as Ms. Hendry acquired subject data; Dr. Rajakumar provided feedback and guidance in the context of neuroanatomy both in the study design as well as the following analyses. With feedback and guidance from Ms. Hendry, Densmore and Drs. Nicolson and Th´eberge, I designed and performed the data analyses, including the development of all computer code, and wrote the manuscripts. For the study of chapter 3 assessing the feasibility of obtaining myelin water fraction measurements using a gradient echo sequence, data were acquired by Dr. DeVito and myself; Drs. Drost, Th´eberge, and Gelman provided technical feedback and guidance. I developed the computer code and performed all data analyses, under the supervision of Drs. Th´eberge and Nicolson. For the study of chapter 3 assessing the feasibility of quantifying the myelin water fraction using multi-component gradient echo sampling of spin echoes (mcGESSE) using simulations, I developed the theory, the acquisition strategy, the computer code used for the simulations, and the data fitting routine under the supervision of Drs. Th´eberge and Nicolson. For the study of chapter 4, outlining the in vivo implementation of mcGESSE, I performed the pulse sequence programming, designed the acquisition protocol, im- proved the data fitting routine with the technical guidance of Dr. Th´eberge. I re- cruited all study participants, acquired all imaging data and performed all analyses, under the supervision of Drs. Th´eberge and Nicolson. iv Acknowledgments I am indebted to the many who have made my journey possible. Foremost, I wish to thank my supervisors, Dick Drost, Rob Nicolson and Jean Th´eberge, who stepped in upon the retirement of Dr. Drost. Their patience and support have allowed me to explore these projects with independence and autonomy. I offer my regards to all those who contributed to this work with feedback, criti- cism, code snippets, or difficult questions. IwouldliketoacknowledgethefinancialsupportIreceivedthroughtheUniversity of Western Ontario, Autism Ontario and the Ontario Mental Health Foundation. Iamforevergratefultomyfamilyandfriendsfortheiremotionalsupportthrough- out my graduate studies. And most of all, my loving and encouraging wife Rae-Anne, who stands by me with unwavering support through all my endeavours. v Contents Abstract ii Co-Authorship iv Acknowledgements v List of Tables xi List of Figures xii List of Acronyms & Abbreviations xiv 1 Autism, Tourette Syndrome and MRI 1 1.1 Thesis introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Autism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.2 Characteristics of autism . . . . . . . . . . . . . . . . . . . . . 4 1.2.3 Magnetic resonance imaging in autism . . . . . . . . . . . . . 5 1.2.4 The under-connectivity hypothesis . . . . . . . . . . . . . . . 7 1.2.5 Neurodevelopment and myelination . . . . . . . . . . . . . . . 8 1.2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Tourette Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.2 Magnetic resonance imaging in Tourette Syndrome . . . . . . 12 1.3.3 Implication of the cortico-striato-thalamo-cortical circuit . . . 12 1.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4 Magnetic Resonance Imaging . . . . . . . . . . . . . . . . . . . . . . 14 1.4.1 Fundamentals of Nuclear Magnetic Resonance . . . . . . . . . 14 1.4.2 The Bloch Equations . . . . . . . . . . . . . . . . . . . . . . . 18 1.4.3 Imaging gradients . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.4.4 Transverse relaxation in vivo . . . . . . . . . . . . . . . . . . . 25 1.4.5 Quantitative T2 . . . . . . . . . . . . . . . . . . . . . . . . . . 26 vi 1.4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2 Investigating T2 in autism and Tourette Syndrome 29 2.1 T2 abnormalities of white matter in autism . . . . . . . . . . . . . . . 29 2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.1.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.1.2.1 Subjects . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.1.2.2 MRI acquisition . . . . . . . . . . . . . . . . . . . . 33 2.1.2.3 Image reconstruction and registration . . . . . . . . . 33 2.1.2.4 Mean lobar T2 calculations . . . . . . . . . . . . . . 35 2.1.2.5 Mean compartmental T2 calculations . . . . . . . . . 37 2.1.2.6 Statistical analysis . . . . . . . . . . . . . . . . . . . 37 2.1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.1.3.1 Lobar . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.1.3.2 Compartmental . . . . . . . . . . . . . . . . . . . . . 42 2.1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.1.4.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . 43 2.1.4.2 Relevance to other imaging studies . . . . . . . . . . 45 2.1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.2 T2 abnormalities of the basal ganglia in Tourette syndrome . . . . . . 47 2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.2.2.1 Subjects . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.2.2.2 MRI acquisition, image reconstruction and image reg- istration . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.2.2.3 Mean T2 measurements . . . . . . . . . . . . . . . . 49 2.2.2.4 Statistical analysis . . . . . . . . . . . . . . . . . . . 50 2.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3 Multi-component T2 in white matter and myelin mapping 56 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.2 Multi-component T2 in human white matter . . . . . . . . . . . . . . 57 3.2.1 Compartment model . . . . . . . . . . . . . . . . . . . . . . . 57 3.2.2 Water exchange . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.2.3 T2 relaxation of brain tissue . . . . . . . . . . . . . . . . . . . 58 3.3 Current multi-component T2 and myelin water mapping strategies . . 60 3.3.1 mcT2: an ill-posed mathematical problem . . . . . . . . . . . 60 3.3.2 Spin echo based methods . . . . . . . . . . . . . . . . . . . . . 61 3.3.2.1 Optimized single slice MESE as the gold standard . . 61 vii 3.3.2.2 Linear combination of spin echoes . . . . . . . . . . . 62 3.3.2.3 MESE with variable TR . . . . . . . . . . . . . . . . 64 3.3.2.4 T2 prep . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.3.2.5 MESE with stimulated echo correction . . . . . . . . 65 3.3.2.6 MESE with 3D GRASE . . . . . . . . . . . . . . . . 65 3.3.3 Gradient echo based methods . . . . . . . . . . . . . . . . . . 66 3.3.4 Steady state based methods . . . . . . . . . . . . . . . . . . . 67 3.4 mcT2* in vivo: feasibility study . . . . . . . . . . . . . . . . . . . . . 67 3.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.4.2.1 Study subject and MRI acquisition . . . . . . . . . . 68 3.4.2.2 Data analysis . . . . . . . . . . . . . . . . . . . . . . 70 3.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3.4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.5 Feasibility of MWF quantification using gradient echo sampling of spin echoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 3.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 3.5.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 3.5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.5.3.1 Simulated model parameters . . . . . . . . . . . . . . 83 3.5.3.2 Simulated mcGESSE acquisition parameters . . . . . 86 3.5.3.3 SNR . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.5.3.4 Iteratively re-weighted robust least squares . . . . . . 87 3.5.3.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . 88 3.5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 3.5.4.1 Inspection of previous 3T gradient echo image . . . . 89 3.5.4.2 Starting estimates . . . . . . . . . . . . . . . . . . . 89 3.5.4.3 Sampling density and receiver bandwidth . . . . . . 89 3.5.4.4 SNR . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 3.5.4.5 Static dephasing (T2(cid:48)) . . . . . . . . . . . . . . . . . 92 3.5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.5.5.1 Summary of results . . . . . . . . . . . . . . . . . . . 93 3.5.5.2 Two-pool model . . . . . . . . . . . . . . . . . . . . 94 3.5.5.3 Influence of starting estimates . . . . . . . . . . . . . 95 3.5.5.4 In-vivo implementation and SNR . . . . . . . . . . . 95 3.5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 viii 4 Myelin mapping of the human brain using mcGESSE 100 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.2.1 Pulse sequence development and MR data acquisition . . . . . 103 4.2.2 Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 4.2.3 mcGESSE data analysis . . . . . . . . . . . . . . . . . . . . . 105 4.2.4 Image normalization and segmentation . . . . . . . . . . . . . 106 4.2.5 Creation of ROI . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.2.6 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . 108 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 4.4.1 Relevance of previous simulations study . . . . . . . . . . . . 115 4.4.2 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 4.4.3 Field inhomogeneities . . . . . . . . . . . . . . . . . . . . . . . 117 4.4.4 Image segmentation and normalization . . . . . . . . . . . . . 118 4.4.5 MWF distributions . . . . . . . . . . . . . . . . . . . . . . . . 119 4.4.6 High-field imaging of white matter . . . . . . . . . . . . . . . 121 4.4.7 Accelerated protocol using parallel imaging . . . . . . . . . . . 121 4.4.8 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 5 Conclusions and summary 123 5.1 T2 abnormalities in autism . . . . . . . . . . . . . . . . . . . . . . . . 124 5.2 T2 abnormalities in Tourette syndrome . . . . . . . . . . . . . . . . . 125 5.3 Feasibility of mcT2* in vivo for myelin mapping . . . . . . . . . . . . 125 5.4 Feasibility of mcGESSE for myelin mapping: simulations . . . . . . . 126 5.5 Myelin mapping in vivo using mcGESSE . . . . . . . . . . . . . . . . 127 5.6 Significance of this work . . . . . . . . . . . . . . . . . . . . . . . . . 128 5.7 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Bibliography 130 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Appendices 154 I Power image noise correction 155 II Iteratively re-weighted robust least squares 157 IIIEthics approval for the study of autism using MRI 158 IVEthics approval for the study of TS using MRI 159 ix