Keywords: Data Analysis, Machine Learning/Artificial Intelligence, Stroke diffusion modelingLimited prior work has explored longitudinal modeling of human brain stroke using advanced diffusion techniques. We aim to address this gap by analyzing longitudinal stroke data from diffusion spectrum imaging for modeling and predicting clinical markers of stroke recovery. Our proposed data analysis method uses a mixed-effect model which exploits stochastic distances from these images for improved regression model statistics and handling of imbalanced, inconsistent longitudinal data. We also demonstrate that this aids in differentiating between population-level and patient-level effects and the corresponding key contributing predictors at each of these levels.
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