Improving the specificity of quantitative neuroimaging biomarkers for monitoring disease progression and understanding disease mechanisms in multiple sclerosis with diffusion magnetic resonance imaging

Abstract

Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease of the central nervous system (CNS) that is associated with progressive neurodegeneration. To better understand the dynamics of disease progression in individuals with MS, and to personalise treatment strategies, the development of quantitative in-vivo biomarkers is critical. Magnetic resonance imaging (MRI) is an essential technique that has been successfully embedded in the formal diagnostic criteria for MS since 2001. Conventional MRI techniques facilitate the demonstration of lesion dissemination in both space and time. Furthermore, conventional MRI metrics derived from quantitative analysis can predict disability progression in clinical trials. However, these imaging metrics are often criticised for their weak correlations with clinical outcomes at the individual level; and lack of specificity for the underlying pathological process(es). Despite successes in large group studies, the transition of quantitative neuroimaging to clinical practice as a tool for both monitoring disease progression and guiding therapeutic strategy has progressed slowly. In this thesis, a refined analysis framework that improves the specificity and clinical validity of MRI metrics, is described and evaluated. Specifically, multi-modal approaches that integrate advanced diffusion imaging and conventional structural metrics are investigated; and a potential composite biomarker of MS disease progression is proposed.