Myelin water imaging in health and disease - techniques, applications, and multimodal integration

Neuroimaging with magnetic resonance imaging (MRI) has made great contributions to our understanding of neurological diseases. Among the many different imaging techniques, myelin water imaging (MWI) appears to be particularly promising for investigating white matter microstructure, particularly in terms of its myelin content. MWI has shown great success in identifying and characterising alterations of myelin content in neurological diseases but is still only available in research settings. In order to bring it closer to clinical practice, its utility, efficacy, and robustness need to be examined. In this work, we investigated the utility of MWI by applying it to Parkinsons disease (PD), a neurodegenerative disease with typically unremarkable changes in the white matter in a clinical setting. We show that MWI and data-driven multivariate analysis methods can predict distinct PD symptom domains. Furthermore, we have demonstrated a robust relation between myelin, cognitive performance and clinical characteristics in Multiple Sclerosis (MS) with a data fusion analysis that finds joint patterns of covariation among the different modalities. Additionally, we have devised new methods to analyse MWI images that not only offer more information about the white matter microstructure, but also make use of complementary information of multimodal MRI experiments. We have demonstrated a characteristic myelin pattern along major white matter fibre bundles that shows superior accuracy in classification of sex than traditional analysis. We have also shown that MWI can be linked to the topological organisation of functional brain networks, either on its own or in combination with other parameters characterising the white matter microstructure. Lastly, we have devised a novel method that makes use of spatiotemporal similarity of white matter voxels in order to denoise MWI data. This method leads to spatially-smoother myelin maps and prove to be more robust in the presence of noise, ultimately leading to more accurate in vivo measurements of myelin in the brain. In summary, we have shown the utility of MWI by applying it to neurodegenerative diseases, developed methods to leverage joint information of multimodal white matter imaging techniques, and proposed a novel method to denoise T2 relaxation data.Applied Science, Faculty ofBiomedical Engineering, School ofGraduat