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The intra-axonal water exchange time {\tau}i, a parameter associated withaxonal permeability, could be an important biomarker for understandingdemyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI issensitive to changes in permeability, however, the parameter has remainedelusive due to the intractability of the mathematical models that incorporateit. Machine learning based computational models can potentially be used toestimate such parameters, and recently, a theoretical framework using a randomforest (RF) suggests this is a promising approach. In this study, we adopt suchan RF approach and experimentally investigate its suitability as a biomarkerfor demyelinating pathologies through direct comparison with histology. Forthis, we use an in-vivo cuprizone (CPZ) mouse model of demyelination withavailable ex-vivo electron microscopy (EM) data. We test our model onnoise-free simulations and find very strong correlations between the predictedand ground truth parameters. For realistic noise levels as in our in-vivo data,the performance is affected, however, the parameters are still well estimated.We apply our RF model on in-vivo data from 8 CPZ and 8 wild-type (WT) mice andvalidate the RF estimates using histology. We find a strong correlation betweenthe in-vivo RF estimates of {\tau}i and the EM measurements of myelin thickness({\rho_\tau}i = 0.82), and between RF estimates and EM measurements ofintra-axonal volume fraction ({\rho_f} = 0.98). When comparing {\tau}i in CPZand WT mice we find a statistically significant decrease in the corpus callosumof the CPZ compared to the WT mice, in line with our expectations that {\tau}iis lower in regions where the myelin sheath is damaged. Overall, these resultsdemonstrate the suitability of machine learning compartment models withpermeability as a potential biomarker for demyelinating pathologies.

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination