Intra- and extra-axonal axial diffusivities in the white matter: Which one is faster?

Abstract

A two-compartment model of diffusion in white matter, which accounts for intra- and extra-axonal spaces, is associated with two plausible mathematical scenarios: either the intra-axonal axial diffusivity D is higher than the extra-axonal D (Branch 1), or the opposite, i.e. D < D (Branch 2). This duality calls for an independent validation of compartment axial diffusivities, to determine which of the two cases holds. The aim of the present study was to use an intracerebroventricular injection of a gadolinium-based contrast agent to selectively reduce the extracellular water signal in the rat brain, and compare diffusion metrics in the genu of the corpus callosum before and after gadolinium infusion. The diffusion metrics considered were diffusion and kurtosis tensor metrics, as well as compartment-specific estimates of the WMTI-Watson two-compartment model. A strong decrease in genu T and T relaxation times post-Gd was observed (p < 0.001), as well as an increase of 48% in radial kurtosis (p < 0.05), which implies that the relative fraction of extracellular water signal was selectively decreased. This was further supported by a significant increase in intra-axonal water fraction as estimated from the two-compartment model, for both branches (p < 0.01 for Branch 1, p < 0.05 for Branch 2). However, pre-Gd estimates of axon dispersion in Branch 1 agreed better with literature than those of Branch 2. Furthermore, comparison of post-Gd changes in diffusivity and dispersion between data and simulations further supported Branch 1 as the biologically plausible solution, i.e. D > D. This result is fully consistent with other recent measurements of compartment axial diffusivities that used entirely different approaches, such as diffusion tensor encoding.

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“…5,36 By comparison, in our spheric mean model, we explicitly estimated the extra-axonal hindered diffusivity, constraining it to be less than the intra-axonal parallel diffusivity, as suggested by independent validation of the compartment parallel diffusivities. 37 In our work, we also took advantage of the 300-mT/m maximum gradient strength accessible on the Connectom scanner. Higher gradient strengths have been shown to increase the sensitivity of diffusion MRI experiments to intra-axonal water diffusion.…”

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“…An advantage of our method, in contrast to alternative SDE techniques, is the simplicity with which the intra‐axonal diffusivity is obtained. In fact, with the TDE approach, D a is the easiest parameter to determine rather than, as other studies have found, one of the most difficult . Once D a is known, then the axonal water fraction, f , can be readily calculated from Equation , which also requires data for the average b0 signal (i.e., ).…”

“…Parameters are estimated by fitting dMRI data, often using nonlinear optimization methods. In WM, one such parameter, intrinsic intra‐axonal diffusivity ( D a ), has proven especially challenging to measure . This is because D a estimates are sensitive to modeling assumptions and tend to be only loosely constrained by the data, although some recently proposed approaches seem to be more promising …”

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“…20,21 This was done to model two scenarios: D ex, 0 < D in, 0 and D in, 0 < D ex, 0 , since it is not completely clear yet whether intra-axonal water is faster than extra-axonal water, although some preliminary evidence leans towards intra-axonal being faster. 48,49…”