Towards a representative reference for MRI-based human axon radius assessment using light microscopy.

Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (r). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (r) on small ensembles of axons, it is unsuited to estimate the tail-weighted r. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for r. In a human corpus callosum, we assessed estimation accuracy and bias of r and r. Furthermore, we investigated whether mapping anatomy-related variation of r and r is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in r. Compared to r, r was estimated with higher accuracy (maximum normalized-root-mean-square-error of r: 8.5 %; r: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of r: 4.8 %; r: 13.4 %). While r was confounded by variation of the image intensity, variation of r seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to r. In conclusion, the proposed method is a step towards representatively estimating r at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.