Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography
Abstract Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in...
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BMC
2024-02-01
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Series: | Acta Neuropathologica Communications |
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Online Access: | https://doi.org/10.1186/s40478-024-01732-z |
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author | Da Ma Wenyu Deng Zain Khera Thajunnisa A. Sajitha Xinlei Wang Gadi Wollstein Joel S. Schuman Sieun Lee Haolun Shi Myeong Jin Ju Joanne Matsubara Mirza Faisal Beg Marinko Sarunic Rebecca M. Sappington Kevin C. Chan |
author_facet | Da Ma Wenyu Deng Zain Khera Thajunnisa A. Sajitha Xinlei Wang Gadi Wollstein Joel S. Schuman Sieun Lee Haolun Shi Myeong Jin Ju Joanne Matsubara Mirza Faisal Beg Marinko Sarunic Rebecca M. Sappington Kevin C. Chan |
author_sort | Da Ma |
collection | DOAJ |
description | Abstract Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity. |
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spelling | doaj.art-94d8962b31084b0ca53d23684c47756a2024-03-05T20:40:36ZengBMCActa Neuropathologica Communications2051-59602024-02-0112111610.1186/s40478-024-01732-zEarly inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomographyDa Ma0Wenyu Deng1Zain Khera2Thajunnisa A. Sajitha3Xinlei Wang4Gadi Wollstein5Joel S. Schuman6Sieun Lee7Haolun Shi8Myeong Jin Ju9Joanne Matsubara10Mirza Faisal Beg11Marinko Sarunic12Rebecca M. Sappington13Kevin C. Chan14Wake Forest University School of MedicineDepartment of Ophthalmology, NYU Grossman School of Medicine, NYU Langone Health, New York UniversityDepartment of Ophthalmology, NYU Grossman School of Medicine, NYU Langone Health, New York UniversityDepartment of Ophthalmology, NYU Grossman School of Medicine, NYU Langone Health, New York UniversityDepartment of Ophthalmology, NYU Grossman School of Medicine, NYU Langone Health, New York UniversityDepartment of Ophthalmology, NYU Grossman School of Medicine, NYU Langone Health, New York UniversityDepartment of Ophthalmology, NYU Grossman School of Medicine, NYU Langone Health, New York UniversitySchool of Engineering Science, Simon Fraser UniversityDepartment of Statistics and Actuarial Science, Simon Fraser UniversityDepartment of Ophthalmology and Visual Sciences, The University of British ColumbiaDepartment of Ophthalmology and Visual Sciences, The University of British ColumbiaSchool of Engineering Science, Simon Fraser UniversityInstitute of Ophthalmology, University College LondonWake Forest University School of MedicineDepartment of Ophthalmology, NYU Grossman School of Medicine, NYU Langone Health, New York UniversityAbstract Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.https://doi.org/10.1186/s40478-024-01732-zDeep learningExcitotoxicityN-methyl-d-aspartateOptical coherence tomographyRetinal thicknessTransfer learning |
spellingShingle | Da Ma Wenyu Deng Zain Khera Thajunnisa A. Sajitha Xinlei Wang Gadi Wollstein Joel S. Schuman Sieun Lee Haolun Shi Myeong Jin Ju Joanne Matsubara Mirza Faisal Beg Marinko Sarunic Rebecca M. Sappington Kevin C. Chan Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography Acta Neuropathologica Communications Deep learning Excitotoxicity N-methyl-d-aspartate Optical coherence tomography Retinal thickness Transfer learning |
title | Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography |
title_full | Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography |
title_fullStr | Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography |
title_full_unstemmed | Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography |
title_short | Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography |
title_sort | early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning assisted optical coherence tomography |
topic | Deep learning Excitotoxicity N-methyl-d-aspartate Optical coherence tomography Retinal thickness Transfer learning |
url | https://doi.org/10.1186/s40478-024-01732-z |
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