Monitoring of myelin damage and repair is of great clinical importance for demyelinating diseases. Conventional MRI measures (e.g. T₂, MTR and radial diffusivity (RD)) are sensitive, but non-specific to the WM injury, whereas WM tract integrity (WMTI) metrics¹ derived from diffusion kurtosis imaging (DKI) can provide compartment specific estimates of WM properties, such as axonal water fraction (AWF) and extra-axonal radial diffusivity (D_e,┴) (Fig. 1). Cuprizone fed mice provide an excellent model of demyelination². In vivo changes in both conventional MRI measures and WMTI parameters during cuprizone-induced demyelination and subsequent recovery were previously reported in the mouse corpus callosum^3-5. Here, we examine the correlations between WMTI parameters and electron microscopy (EM) measures.We conducted an 18-week longitudinal study on 34 female C57 mice (8 weeks old at baseline) separated into 3 groups: "Control" group received standard chow; "Cpz6" and "Cpz12" groups received cuprizone for 6 and 12 weeks, respectively (Fig. 2). MRI: In vivo MRI was performed using a 7-T Bruker BioSpec system. Diffusion-weighted images of one 0.8-mm thick mid-sagittal slice were acquired (b=1000 and 2000 s/mm², 30 directions each, and 6 b=0 images) at 112-μm in-plane resolution. DKI analysis and derivation of the WMTI parameters were performed¹. The splenium was segmented using a semi-automated in-house software tool. EM: Subsets of animals were sacrificed and perfusion-fixed at 6, 12 and 18 weeks (Fig. 2). The brains were extracted and further prepared for EM. Nanometer-thick slices from the splenium were imaged using a Phillips CM12 transmission electron microscope at 2.36 nm spatial resolution. Electron micrographs were analyzed using in-house developed macros in ImageJ and CellProfiler. The EM metrics extracted were myelin volume fraction (MVF), myelinated axon volume fraction (mAVF) and total axonal volume fraction (tAVF). An "aggregate" g-ratio was derived as $$$\sqrt{tAVF/(MVF+tAVF)}$$$. Because there is a conceptual difference between AVF and AWF (the former refers to a volume fraction, the latter is a fraction of "MR visible" water, which does not account for the myelin space), the EM-derived $$$mAWF=mAVF/(1-MVF)$$$ and $$$tAWF=tAVF/(1-MVF)$$$ were further calculated. The EM image analysis was performed for three randomly chosen mice per timepoint and diet group, and eight micrographs per animal, resulting in 423 myelinated and 1623 unmyelinated axons processed on average per animal. Correlations: The mean and standard deviation in the splenium were calculated for all MRI and EM measures in each animal. Partial Spearman correlations (ρ) were calculated between the MRI and the EM measures, using mouse weight at baseline as a covariate. A linear (Pearson) correlation (r) was also computed between WMTI-derived AWF and EM-derived tAWF to evaluate their direct agreement.

All EM and MRI measures were consistent with expected trends of cuprizone intoxication and recovery at all timepoints (Figs. 3 and 4). Acute cuprizone exposure (6 weeks) was characterized by "patchy" demyelination (Figs. 1C and 3A) and significant decrease in AWF, and chronic exposure (12 weeks) by widespread demyelination and significant increase in D_e,┴.

Correlations are summarized in Figure 5. RD correlated significantly with EM-derived MVF, mAWF, tAWF and g-ratio. Among WMTI metrics, the AWF correlated most strongly with tAWF, followed by mAWF and MVF; AWF did not correlate significantly with the g-ratio (although a trend was present). Conversely, D_e,┴ correlated most strongly with MVF and g-ratio; D_e,┴ did not correlate significantly with mAWF and tAWF.

The correlations are consistent with results from previous simulations⁶, which suggest AWF is particularly sensitive to random axonal loss and/or patchy demyelination, while D_e,┴ is particularly sensitive to homogeneous demyelination, and hence to the g-ratio .

The WMTI-derived AWF is by design a measure of the same quantity as the EM-derived tAWF. Remarkably, the two measures exhibited a very strong linear correlation, and their relationship was well fit by a linear model with a quasi-zero intercept (0.014 ± 0.120). The slope however was not unity (0.58 ± 0.17), indicative of a scaling effect between the two modalities. One explanation for this scaling could be non-linear shrinkage caused by chemical fixation in the EM images: the extra-axonal space shrank more than the intra-axonal space. Another explanation, this time affecting AWF_WMTI, could be a higher transverse relaxation rate in the intra- vs. the extra-axonal space⁷ which would introduce a non-negligible $$$e^{-TE\cdot\Delta R_{2}}$$$ weighting.

The correlations between EM and WMTI metrics support the specificity associated with AWF and D_e,┴ as expected from the model. The WMTI model parameters can be derived from data acquired in under ten minutes on clinical scanners, and are therefore promising candidates as sensitive and specific biomarkers for routine monitoring of microstructural changes in demyelinating pathologies.