C. Ross Ethier | Searcharxiv

arXiv2026

Myelin Distribution at the Optic Nerve Myelination Transition Zone Influences Axonal Biomechanics

Purpose: The lamina cribrosa (LC) is considered the initial site of glaucomatous retinal ganglion cell (RGC) injury, and is also the region where unmyelinated RGC axons become myelinated. Here we sought to use finite element (FE) modeling to investigate how the configuration of the myelination transition zone (MTZ) influences the mechanical insult to RGC axons. Methods: A multiscale FE framework was developed to investigate the biomechanical effect of myelin distribution on IOP-induced axonal stress and strain at the MTZ. An anatomically based macro-scale FE eye model was used to compute LC deformations under 15 and 45 mmHg IOP. These deformations were then applied to micro-scale models of the posterior LC, consisting of axons, myelin sheaths, and surrounding matrix. Four distinct MTZ boundary configurations were simulated: one flat and three with random posterior offsets of 3, 6, or 9 μm, representing potential physiological variations. IOP-induced effective axonal strains and stresses were quantified across the different MTZ configurations. Results: Under IOP loading, axons exhibited longitudinal compression and transverse stretch, with marked effective stress and strain discontinuities at the myelin boundary. Across all models, the unmyelinated region exhibited higher effective stress and strain than the myelinated region, and this mechanical discontinuity increased with larger MTZ offsets. Conclusions: Glaucoma-associated demyelination has been previously suggested to precede RNFL thinning. Here we have shown that the MTZ configuration directly influences RGC axonal mechanics. Whether different MTZ profiles can initiate glaucomatous injury, whether demyelination accelerates disease progression, or whether both mechanisms contribute, remains to be determined.

cs.CE↗

arXiv2019

AxoNet: an AI-based tool to count retinal ganglion cell axons

Goal: In this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in images of optic nerve tissue from various animal models of glaucoma. Methods: The U-Net convolutional neural network architecture was adapted to learn pixelwise axon count density estimates, which were then integrated over the image area to determine axon counts. The tool, termed AxoNet, was trained and evaluated using a dataset containing images of optic nerve regions randomly selected from complete cross sections of intact rat optic nerves and manually annotated for axon count and location. Both control and damaged optic nerves were used. This rat-trained network was then applied to a separate dataset of non-human primate (NHP) optic nerve images. AxoNet was then compared to two existing automated axon counting tools, AxonMaster and AxonJ, using both datasets. Results: AxoNet outperformed the existing tools on both the rat and NHP optic nerve datasets as judged by mean absolute error, R2 values when regressing automated vs. manual counts, and Bland-Altman analysis. Conclusion: The proposed tool allows for accurate quantification of axon numbers as a measure of glaucomatous damage. AxoNet is robust to variations in optic nerve tissue damage extent, image quality, and species of mammal. Significance: The deep learning method does not rely on hand-crafted image features for axon recognition. Therefore, this approach is not species-specific and can be extended to quantify additional optic nerve features. It will aid evaluation of optic nerve changes in glaucoma and potentially other neurodegenerative diseases.

q-bio.QM↗