Our research aims to explore the relationship between various imaging and non-imaging biomarkers and their potential as predictors of disease progression in multiple sclerosis (MS). We will utilize the UK Biobank dataset as a control group to compare with our longitudinal cohorts of individuals with MS. The primary goal is to identify markers that can predict the progression of physical and cognitive disability in MS.
Gradual cognitive and physical decline in MS often begins early in the disease course, yet it lacks targeted therapies. Our study aims to fill this gap by focusing on neuroimaging biomarkers that can illuminate the neural basis of this progressive decline in MS. By detecting brain changes early in the disease course, we hope to improve risk stratification and guide timely interventions. The integration of these neuroimaging markers with clinical data has the potential to inform treatment decisions and drive the development of much-needed management for progressive disability in MS.
We will analyze T1-weighted MRI data to assess brain structure. We will consider brain volumes, including total brain volume, WM volume, cortical volume, and thalamic volume, as well as cortical thickness, T1/T2-weighted ratio, brain age, and curvedness. In parallel, we will investigate non-imaging biomarkers such as systemic markers of biological aging, including leukocyte telomere length, and their association with physical and cognitive performance.
By comparing the UK Biobank healthy population with several MS cohorts, we aim to understand the factors contributing to accelerated aging and disability progression in MS. This research will integrate imaging and clinical data using machine learning techniques to develop predictive models that can be applied to both research and clinical settings, ultimately improving our understanding of the aging impact on MS.