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Structural magnetic resonance imaging (MRI) can be used to detect lesions inthe brains of multiple sclerosis (MS) patients. The formation of these lesionsis a complex process involving inflammation, tissue damage, and tissue repair,all of which are visible on MRI. Here we characterize the lesion formationprocess on longitudinal, multi-sequence structural MRI from 34 MS patients andrelate the longitudinal changes we observe within lesions to therapeuticinterventions. In this article, we first outline a pipeline to extract voxellevel, multi-sequence longitudinal profiles from four MRI sequences withinlesion tissue. We then propose two models to relate clinical covariates to thelongitudinal profiles. The first model is a principal component analysis (PCA)regression model, which collapses the information from all four profiles into ascalar value. We find that the score on the first PC identifies areas of slow,long-term intensity changes within the lesion at a voxel level, as validated bytwo experienced clinicians, a neuroradiologist and a neurologist. On a qualityscale of 1 to 4 (4 being the highest) the neuroradiologist gave the score onthe first PC a median rating of 4 (95% CI: [4,4]), and the neurologist gave ita median rating of 3 (95% CI: [3,3]). In the PCA regression model, we find thattreatment with disease modifying therapies (p-value < 0.01), steroids (p-value< 0.01), and being closer to the boundary of abnormal signal intensity (p-value< 0.01) are associated with a return of a voxel to intensity values closer tothat of normal-appearing tissue. The second model is a function-on-scalarregression, which allows for assessment of the individual time points at whichthe covariates are associated with the profiles. In the function-on-scalarregression both age and distance to the boundary were found to have astatistically significant association with the profiles.

Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions