High-Resolution Multi-Depth Quantification of the Retinal Nerve Fiber Layer Builds a Normative Database: 18 Healthy Volunteers Across Three Studies, With Bundles on Average 12 Percent Wider at Shallower Depths, Published in Scientific Reports on November 13
Scientific Reports published a study on November 13, 2026 presenting high-resolution multi-depth quantification of the retinal nerve fiber layer and a normative database for it, under DOI 10.1038/s41598-026-78810-w. The retinal nerve fiber layer, composed of axon bundles converging toward the optic nerve head, has a thickness that is a key biomarker for diagnosing and monitoring glaucoma and neurodegenerative diseases. High-resolution en face imaging of individual nerve fiber bundles offers morphological information beyond conventional optical coherence tomography, yet automated analysis tools and normative data have been lacking. The researchers developed image processing pipelines for automated quantification of bundle trajectory, tortuosity, orientation and width, and established a normative database from 18 healthy volunteers enrolled across three studies, with group sizes of 15, 11 and 11, imaged using adaptive optics scanning laser ophthalmoscopy and full-field optical coherence tomography. Trajectory analysis revealed systematic deviations from existing mathematical models, leading the team to propose two refined models. Bundles were on average 12 percent wider at shallower retinal depths, and bundle width decreased significantly with age. The paper, by Clémentine Callet, Maxime Bertrand, Kate Grieve and co-authors, was received on July 16, 2026 and accepted on October 28, 2026. The pipelines and normative data are intended as a reference for later imaging research on glaucoma and neurodegenerative diseases.
(Source: Scientific)
Source: Scientific
Published by GSM · USA (original Chinese edition)
Photo: 图：眼科检查场景。U.S. Air Force photo by Andrea Jenkins，公共领域，经 Wikimedia Commons。 / Photo: U.S. Air Force photo by Andrea Jenkins, public domain, via Wikimedia Commons.
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