Isabel Ellerbrock^{1} and Siawoosh Mohammadi^{1,2,3}

The g-ratio, the ratio between the inner and outer diameter of a myelinated axon, is of great neuroscientific interest because it is a relative measure of axonal myelination and functionally linked to conduction velocity. In vivo g-ratio mapping has been recently suggested using a flexible biophysical model that relates the microscopic g-ratio, only accessible by histology, to MRI biomarkers for the myelin and fiber compartment. This study investigates the question which MRI biomarker is optimal for MR g-ratio mapping concerning precision (determined by scan-rescan reproducibility) and accuracy (assessed by comparability to previous in vivo and the ex vivo results).

An extensive quantitative MRI (qMRI) protocol acquired the biomarkers for calculating MR g-ratios, multi-parameter mapping (6) and multi-shell diffusion-weighted imaging. The protocol involved two distinct acquisition techniques for imaging MVF: (i) the macromolecular tissue volume (MTV) based on proton density imaging (7) and (ii) magnetization transfer (MT) saturation imaging (8), and two techniques for imaging FVF: (i) neurite orientation dispersion and density imaging (4) (NODDI) and (ii) tract-fiber density (9) (TFD). MRI was performed on a 3T Tim TRIO system (Siemens, Erlangen), data analyses were performed using MATLAB (The MathWorks, Natick, MA, R2014b) and statistical parametric mapping (SPM12, London). Four g-ratio indices were calculated for each subject and session using different combinations of the qMRI biomarkers for MVF and FVF (fig. 1), based on the function:

$$g=\sqrt{1-\frac{MVF} {FVF}}$$

For each MR g-ratio metric, a customized coefficient of variation (CoV) map was created ((<g>)/SD) (fig. 2). To exclude voxels with high partial volume effects, a mask was generated based on the CoV map using the threshold previously proposed (3): 0 < CoV < 0.3. The precision was assessed via the reproducibility of MR g-ratio metrics in a scan-rescan design (6-8 days interval) in 12 healthy volunteers. G-ratios were calculated within specific white matter fiber tracts, defined in the Anatomy toolbox (10). In an additional analysis, the CoV mask was not inserted, i.e. partial volume effects were not restricted.

The accuracy was tested by comparison with in vivo MR g-ratio (3) and ex vivo microscopic g-ratio (4). For the latter, group-mean MR g-ratio values were determined in eight regions of interest (ROIs) within the corpus callosum (fig. 5A) for comparison with histological ex vivo macaque monkey data (4). Analogous to the previous analysis, the CoV mask was used to reduce partial volume effects and the same analysis was performed without partial volume effect correction.

(1) Stikov N, Perry LM, Mezer A, et al. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. Neuroimage. 2011;54(2), 1112-1121.

(2) Campbell J, Leppert I, Boudreau M, et al. Mapping the myelin g-ratio: promises and pitfalls. Presented at the Proc. Intl. Soc. Mag. Reson. Med. 24. 2016; abstract:1501. 3 Mohammadi S, Carey D, Dick F, et al. Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers. Front. Neurosci. 2015;9(441).

(4) Stikov N, Campbell JSW, Stroh T, et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage. 2015;118:397–405.

(5) Duval T, Lévy S, Stikov N, et al. g-Ratio weighted imaging of the human spinal cord in vivo. Neuroimage. In press. Available online 22 September 2016.

(6) Weiskopf N, Suckling J, Williams G, et al. Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation. Front. Neurosci. 2013;7(95).

(7) Mezer A, Yeatman JD, Stikov N, et al. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat. Med. 2013;19(12):1667-1672.

(8) Helms G, Dathe H, Kallenberg K, et al. High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI. Magn. Reson. Med. 2008;60(6):1396-1407.

(9) Reisert M, Mader I, Umarova R, et al. Fiber density estimation from single q-shell diffusion imaging by tensor divergence. Neuroimage. 2013;77:166–176.

(10) Eickhoff SB, Stephan KE, Mohlberg H, et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage. 2005;25(4):1325–1335.