Advanced magnetic resonance imaging in multiple sclerosis: disentangling aging and pathology effects

Multiple sclerosis is a chronic neuroinflammatory disease characterized by demyelination, axonal loss, and neurodegeneration within the central nervous system. To date, the improvement of various immunomodulatory treatments has extended the lifespan of people with multiple sclerosis, increasing the average age of this population. Both aging and multiple sclerosis pathology lead to neurodegeneration, therefore distinguishing the effects of normal aging from disease-specific pathology is particularly challenging.
To address these challenges, we have developed a comprehensive framework to investigate multiple sclerosis-specific pathology and multiple sclerosis-related aging using advanced MRI techniques. We first modeled the influence of age on quantitative MRI measures and then used this to adjust for age dependency and isolate multiple sclerosis-specific pathology. Secondly, we derived normative age trajectories of quantitative MRI measures in healthy individuals, to better understand the aging effect on brain microstructure. Finally, we used machine learning methods to predict multiple sclerosis patients’ brain age using morphological and quantitative measurements, demonstrating a significant association between predicted deviations to chronological age with disease progression in multiple sclerosis patients. Our results provided new insights into the complex interplay between aging and pathology in multiple sclerosis patients and offered a window into mechanisms driving clinical worsening.
This doctoral thesis lays therefore a strong foundation for future work aimed at therapeutically targeting pathological aging in patients with multiple sclerosis.