In vivo quantification of demyelination and recovery using compartment-specific diffusion MRI metrics validated by electron microscopy

Abstract

There is a need for accurate quantitative non-invasive biomarkers to monitor myelin pathology in vivo and distinguish myelin changes from other pathological features including inflammation and axonal loss. Conventional MRI metrics such as T2, magnetization transfer ratio and radial diffusivity have proven sensitivity but not specificity. In highly coherent white matter bundles, compartment-specific white matter tract integrity (WMTI) metrics can be directly derived from the diffusion and kurtosis tensors: axonal water fraction, intra-axonal diffusivity, and extra-axonal radial and axial diffusivities. We evaluate the potential of WMTI to quantify demyelination by monitoring the effects of both acute (6 weeks) and chronic (12 weeks) cuprizone intoxication and subsequent recovery in the mouse corpus callosum, and compare its performance with that of conventional metrics (T2, magnetization transfer, and DTI parameters). The changes observed in vivo correlated with those obtained from quantitative electron microscopy image analysis. A 6-week intoxication produced a significant decrease in axonal water fraction (p < 0.001), with only mild changes in extra-axonal radial diffusivity, consistent with patchy demyelination, while a 12-week intoxication caused a more marked decrease in extra-axonal radial diffusivity (p = 0.0135), consistent with more severe demyelination and clearance of the extra-axonal space. Results thus revealed increased specificity of the axonal water fraction and extra-axonal radial diffusivity parameters to different degrees and patterns of demyelination. The specificities of these parameters were corroborated by their respective correlations with microstructural features: the axonal water fraction correlated significantly with the electron microscopy derived total axonal water fraction (ρ = 0.66; p = 0.0014) but not with the g-ratio, while the extra-axonal radial diffusivity correlated with the g-ratio (ρ = 0.48; p = 0.0342) but not with the electron microscopy derived axonal water fraction. These parameters represent promising candidates as clinically feasible biomarkers of demyelination and remyelination in the white matter.

Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

“…In contrast, NDI always drops dramatically within lesions, similar to myelin density MSF. This finding is not surprising as NDI is a surrogate index equivalent to NSF/(1–MSF)26, 43 (post hoc analysis confirms a correlation between these two measures). Although altered exchange of water between intra/extra‐axonal space (i.e., permeability)40, 44 may have contributed to the observed patterns of NDI, it is expected that variations of myelin density would also contribute.…”

“…Future validation of our preliminary findings could include: analysis of tissue from areas beyond the spinal cord; extension of the histology to the third dimension50, 51; further confirmation from in vivo data; characterization of more complex morphological features of glial cells52; more accurate diffusion MRI signal modeling43, 53; analysis of other quantitative MRI metrics (such as those from relaxometry, magnetization transfer techniques or susceptibility imaging).…”

“…It is possible that other indices, either from other diffusion techniques19, 43 or from other MRI modalities (relaxometry, magnetization transfer techniques, susceptibility imaging or others) may have shown even stronger histopathological correlations. The main focus of this work was neurite orientation dispersion.…”

Recent Developments in Fast Kurtosis Imaging

“…Validation studies comparing WMTI indices to histology and electron microscopy [23][24][25][26] have largely confirmed the ability of WMTI to detect microstructural changes in WM. While other frameworks for DKI-based estimation of tissue model parameters have been proposed [27,28], a recent comparative study showed WMTI to be the most widely applicable [27].…”

Studying neuroanatomy using MRI

“…More complex biophysical models along with sophisticated measurements can more precisely explain the measured signal 119 and aid interpretation by providing specific markers of microstructure changes [120][121][122] . For example, the diffusion signal measured at a range of q values (gradient areas) exhibits a characteristic diffraction pattern in which zero crossings indicate the size of restricted compartments 123 .…”