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

TL;DR

This study demonstrates that machine learning models can accurately estimate axonal permeability parameters from diffusion MRI data, correlating strongly with histological measures and serving as potential biomarkers for demyelinating diseases like Multiple Sclerosis.

Contribution

The paper introduces a machine learning approach, specifically a random forest model, to estimate intra-axonal water exchange time as a biomarker for demyelination, validated with in-vivo mouse data and histology.

Findings

Strong correlation between RF estimates and EM measurements ({ ho_ au}i=0.82, { ho_f}=0.98)

RF model accurately estimates parameters even with realistic noise levels

Significant decrease in { au}i in demyelinated mice compared to controls

Abstract

The intra-axonal water exchange time {\tau}i, a parameter associated with axonal permeability, could be an important biomarker for understanding demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI is sensitive to changes in permeability, however, the parameter has remained elusive due to the intractability of the mathematical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters, and recently, a theoretical framework using a random forest (RF) suggests this is a promising approach. In this study, we adopt such an RF approach and experimentally investigate its suitability as a biomarker for demyelinating pathologies through direct comparison with histology. For this, we use an in-vivo cuprizone (CPZ) mouse model of demyelination with available ex-vivo electron microscopy (EM) data. We test our model on noise-free simulations and find very strong correlations between the predicted and 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 and validate the RF estimates using histology. We find a strong correlation between the 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 of intra-axonal volume fraction ({\rho_f} = 0.98). When comparing {\tau}i in CPZ and WT mice we find a statistically significant decrease in the corpus callosum of the CPZ compared to the WT mice, in line with our expectations that {\tau}i is lower in regions where the myelin sheath is damaged. Overall, these results demonstrate the suitability of machine learning compartment models with permeability as a potential biomarker for demyelinating pathologies.