Axon morphology is modulated by the local environment and impacts the non-invasive investigation of its structure-function relationship

Abstract

Axonal conduction velocity, which ensures efficient function of the brain network, is related to axon diameter. Non-invasive, in vivo axon diameter estimates can be made with diffusion magnetic resonance imaging, but the technique requires 3D validation. Here, high resolution, 3D synchrotron X-ray Nano-Holotomography images of white matter samples from the corpus callosum of a monkey brain reveal that blood vessels, cells and vacuoles affect axonal diameter and trajectory. Within single axons, we find that the variance in diameter and conduction velocity correlates with the mean diameter, contesting the value of precise diameter determination in larger axons. These complex 3D axon morphologies drive previously reported 2D trends in axon diameter and g-ratio. Furthermore, we find that these morphologies bias the estimates of axon diameter with diffusion magnetic resonance imaging and, ultimately, impact the investigation and formulation of the axon structure-function relationship. 4 acquired diffusion signal 15 . However, diffusion MRI-based AD estimates 14,16,17 are larger than those obtained by histology 15,18 . A potential cause is inaccurate modeling of the WM compartments, including the century old representation of myelinated axons as cylinders. A validation of the 3D WM anatomy could thus improve diffusion MRI-based AD estimations 17 and shed light on the validity of enforcing a cylindrical geometry and constant g-ratio in axonal structure-function relations. Recent 3D electron microscopy (EM) studies on axon morphology of the mouse reveal, in high resolution, non-uniform ADs and trajectories 19,20 . However, axons are only tracked for up to 20 µm, a fraction of their length in MRI voxels. Here, we characterize the long-range micro-morphologies of axons against the backdrop of the complex 3D WM environment consisting of blood vessels, cells and vacuoles. With synchrotron X-ray Nano-Holotomography (XNH), we acquire MRI measurements of the WM from the same monkey brain as in Alexander et al. (2010) 14 and Dyrby et al. (2013) 21 , in which the MRIderived AD estimates were larger than those estimated by histology. The 3D WM environment is mapped at a voxel size of 75 nm and volume of approximately 150 ⨉ 150 ⨉ 150 µm 3 . By combining adjacent XNH volumes, we extract axons >660 µm in length and show that AD, axon trajectory and g-ratio depend on the local microstructural environment. The 3D measurements shed light on the interpretation of 2D measurements, highlighting the importance of the third dimension for a robust description of single-axon structure and function. Lastly, by performing Monte Carlo (MC) diffusion simulations on axonal substrates with morphological features deriving from the XNH-segmented axons, we show that geometrical deviations from cylinders cause an overestimation of AD with diffusion MRI.

The variability of MR axon radii estimates in the human white matter

“…Even after eliminating orientational dispersion, it is not understood why we observe such strong along‐tract variability of the MR axon radii mapping in certain tracts (e.g., CST). Various additional confounding factors have been discussed, for example, diameter variations due to curvature or undulations of the axons (Andersson et al, 2020; Brabec, Lasič, & Nilsson, 2020; Lee, Jespersen, et al, 2020; Lee, Paioannou, Kim, Novikov and Fieremans 2020; Nilsson, Lätt, Ståhlberg, van Westen, & Hagslätt, 2012). However, in agreement with our previous hypothesis, this observation might also be explained by the lack of specificity of the high b signal to axons only.…”

“…The sixth‐order in the numerator arises from the combination of biquadratic relation between and r (Neuman, 1974), and of the subsequent volume‐weighting that emphasizes the thickest axons by an extra quadratic factor (Alexander et al, 2010; Packer & Rees, 1972). Therefore, the effective MR radius is heavily weighted by the largest axons within the voxel or, more specifically, the largest Martin's radius in case of non‐cylindrical axons (Andersson et al, 2020).…”

Unraveling micro-architectural modulations in neural tissue upon ischemia by Correlation Tensor MRI

“…Given the longitudinal nature of micro-architectural modulations in neural tissue, non-invasive mapping of tissue microstructure 18,19 plays a pivotal role in assessing enhancements in microstructure (e.g., due to learning) as well as adverse effects upon disease or neural injury. Diffusion-weighted magnetic resonance imaging (DWI) – a non-invasive methodology capable of imparting contrast sensitive to motion of water molecules in the tissue 20 , thereby indirectly probing their interaction with the microscopic boundaries imposed by the cellular environment 21 – has completely revolutionized our ability to follow microstructural modulations over extended periods of time.…”

Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?

“…The CC has also been the subject of light and electron microscopy (EM) studies on AD, and these have been used as validation for the di↵usion MRI-based AD metrics [10,17,18]. However, recent 3D imaging studies in the monkey and mouse CC demonstrate the complex morphologies, OD and trajectory variations of axons [19][20][21], and show how -even in the highly organised CC -these will bias AD measurements [19,20,22,23]. Di↵usion MRI-based estimates of AD should thus take into account three di↵erent classes of orientation e↵ects: 1) the macroscopic fibre architecture, describing the relative orientations of di↵erent fibre bundles e.g.…”

“…The PL implementation from Veraart et al has been evaluated within axon segments of length ⇠ 20 µm from electron microscopy (EM) of the mouse CC using Monte Carlo (MC) simulations of di↵usion [22]. Given that non-axonal structures in the EAS can impact the trajectories of axons for up to 20 µm [19], longer axonal segments may to a greater extent represent the characteristics of the IAS. Notably, although the PA is expected to factor out the e↵ects of fibre crossings and OD, it has only been validated on segments of axons from the CC in which the fibre architecture is simple and does not contain substantial crossings.…”