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

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2022-04, Vol.249, p.118906-118906, Article 118906
• Main Authors: Mordhorst, Laurin, Morozova, Maria, Papazoglou, Sebastian, Fricke, Björn, Oeschger, Jan Malte, Tabarin, Thibault, Rusch, Henriette, Jäger, Carsten, Geyer, Stefan, Weiskopf, Nikolaus, Morawski, Markus, Mohammadi, Siawoosh
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Anatomy; Axon radii distribution; Axons; Axons - ultrastructure; Brain; Brain architecture; Corpus callosum; Cross microscopy; Deep Learning; Estimates; Female; Histology; Humans; Light microscopy; Magnetic Resonance Imaging; Male; Microscopy; Microscopy - methods; Middle Aged; MRI-based axon radius; Nervous system; Neuroanatomy; Neuroimaging - methods; Segmentation; Variation; White Matter - diagnostic imaging; White Matter - ultrastructure
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.118906

•A pipeline for automated estimation of ensemble mean axon radii at MRI voxel scale•Incorporation of 4 human white matter brain tissue samples for training and testing•Accurate mapping of the MRI-visible radius in the presence of staining heterogeneity•Includes 2 to 4 orders of magnitude more axons than the current gold standard (EM)•The pipeline enables validation of biophysical, MRI-based radius estimation models 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 (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff 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 reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.