Axon diameter and myelin thickness affect the conduction velocity of action potentials in the nervous system. Imaging them non-invasively with MRI-based methods is, thus, valuable for studying brain microstructure and function. Electron microscopy studies suggest that axon diameter and myelin thickness are closely related to each other. However, the relationship between MRI-based estimates of these microstructural measures, known to be relative indices, has not been investigated across the brain mainly due to methodological limitations. In recent years, studies using ultra-high-gradient strength diffusion MRI (dMRI) have demonstrated improved estimation of axon diameter index across white-matter (WM) tracts in the human brain, making such investigations feasible. In this study, we aim to investigate relationships between tissue microstructure properties across white-matter tracts, as estimated with MRI-based methods. We collected dMRI with ultra-high-gradient strength and multi-echo spin-echo MRI on ex vivo macaque and human brain samples on a preclinical scanner. From these data, we estimated axon diameter index, intra-axonal signal fraction, myelin water fraction (MWF), and aggregate g-ratio and investigated their correlations. We found that the correlations between axon diameter index and other microstructural imaging parameters were weak but consistent across WM tracts in samples estimated with sufficient signal-to-noise ratio. In well-myelinated regions, tissue voxels with larger axon diameter indices were associated with lower packing density, lower MWF, and a tendency of higher g-ratio. We also found that intra-axonal signal fractions and MWF were not consistently correlated when assessed in different samples. Overall, the findings suggest that MRI-based axon geometry and myelination measures can provide complementary information about fiber morphology, and the relationships between these measures agree with prior electron microscopy studies in smaller field of views. Combining these advanced measures to characterize tissue morphology may help differentiate tissue changes during disease processes such as demyelination versus axonal damage. The regional variations and relationships of microstructural measures in control samples as reported in this study may serve as a point of reference for investigating such tissue changes in disease.

Glioblastoma (GBM) is the most common and aggressive brain tumour with stark resistance to available therapies, leading to relapse and a median survival of <15 months. A key cause of therapy resistance is diffuse infiltration of tumour cells into brain regions surrounding the tumour, which presents a major clinical challenge as existing imaging techniques offer limited detection of the resectable margin. Here, we use diffusion weighted imaging (DWI) and apply the multiple echo time neurite orientation dispersion and density imaging (MTE-NODDI) model as a tool to detect tumour cells in the hard-to-distinguish margin. We used the G144 patient-derived xenograft model, with characteristic invasion along white matter tracts, in combination with MTE-NODDI. Tumour development was monitored, and magnetic resonance imaging (MRI) data were acquired over a 4-week period, starting at 4 weeks after stereotactic injection of tumour cells. MTE-NODDI demonstrated sensitivity to the developing tumour in the invading margin, and changes in measured parameters were apparent from 6 weeks after injection. In comparison to standard DWI, MTE-NODDI showed increased sensitivity to the tumour-associated changes in the margin. Furthermore, extraneurite volume fraction (f en ) and neurite density index (NDI) measured from MTE-NODDI correlated with immunohistological measurement of tumour cells. These findings suggest that MTE-NODDI may non-invasively detect infiltrating cells and tumour-induced pathology in margin regions without T2 or DWI changes in a patient-derived mouse model of GBM. MTE-NODDI is clinically translatable and could be a powerful tool for neurosurgeons to maximise surgical resection, resulting in better survival outcomes for patients with GBM.