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For: Fortune B, Reynaud J, Cull G, Burgoyne CF, Wang L. The Effect of Age on Optic Nerve Axon Counts, SDOCT Scan Quality, and Peripapillary Retinal Nerve Fiber Layer Thickness Measurements in Rhesus Monkeys. Transl Vis Sci Technol 2014;3:2. [PMID: 24932430 DOI: 10.1167/tvst.3.3.2] [Cited by in Crossref: 14] [Cited by in F6Publishing: 17] [Article Influence: 1.8] [Reference Citation Analysis]
Number Citing Articles
1 Saliani A, Perraud B, Duval T, Stikov N, Rossignol S, Cohen-Adad J. Axon and Myelin Morphology in Animal and Human Spinal Cord. Front Neuroanat 2017;11:129. [PMID: 29311857 DOI: 10.3389/fnana.2017.00129] [Cited by in Crossref: 32] [Cited by in F6Publishing: 28] [Article Influence: 6.4] [Reference Citation Analysis]
2 Yucel YH, Gupta N. A framework to explore the visual brain in glaucoma with lessons from models and man. Experimental Eye Research 2015;141:171-8. [DOI: 10.1016/j.exer.2015.07.004] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 1.6] [Reference Citation Analysis]
3 Stebbings KA, Choi HW, Ravindra A, Caspary DM, Turner JG, Llano DA. Ageing-related changes in GABAergic inhibition in mouse auditory cortex, measured using in vitro flavoprotein autofluorescence imaging. J Physiol 2016;594:207-21. [PMID: 26503482 DOI: 10.1113/JP271221] [Cited by in Crossref: 19] [Cited by in F6Publishing: 11] [Article Influence: 2.7] [Reference Citation Analysis]
4 Raza AS, Hood DC. Evaluation of a Method for Estimating Retinal Ganglion Cell Counts Using Visual Fields and Optical Coherence Tomography. Invest Ophthalmol Vis Sci 2015;56:2254-68. [PMID: 25604684 DOI: 10.1167/iovs.14-15952] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 1.3] [Reference Citation Analysis]
5 Fortune B, Cull G, Reynaud J, Wang L, Burgoyne CF. Relating Retinal Ganglion Cell Function and Retinal Nerve Fiber Layer (RNFL) Retardance to Progressive Loss of RNFL Thickness and Optic Nerve Axons in Experimental Glaucoma. Invest Ophthalmol Vis Sci 2015;56:3936-44. [PMID: 26087359 DOI: 10.1167/iovs.15-16548] [Cited by in Crossref: 24] [Cited by in F6Publishing: 26] [Article Influence: 3.4] [Reference Citation Analysis]
6 Fortune B. In vivo imaging methods to assess glaucomatous optic neuropathy. Exp Eye Res 2015;141:139-53. [PMID: 26048475 DOI: 10.1016/j.exer.2015.06.001] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 1.7] [Reference Citation Analysis]
7 Burgoyne CF. The non-human primate experimental glaucoma model. Exp Eye Res 2015;141:57-73. [PMID: 26070984 DOI: 10.1016/j.exer.2015.06.005] [Cited by in Crossref: 25] [Cited by in F6Publishing: 28] [Article Influence: 3.6] [Reference Citation Analysis]
8 Nagarkatti-Gude N, Gardiner SK, Fortune B, Demirel S, Mansberger SL. Optical Coherence Tomography Segmentation Errors of the Retinal Nerve Fiber Layer Persist Over Time. J Glaucoma 2019;28:368-74. [PMID: 30855415 DOI: 10.1097/IJG.0000000000001222] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 Shigueoka LS, Mariottoni EB, Thompson AC, Jammal AA, Costa VP, Medeiros FA. Predicting Age From Optical Coherence Tomography Scans With Deep Learning. Transl Vis Sci Technol 2021;10:12. [PMID: 33510951 DOI: 10.1167/tvst.10.1.12] [Reference Citation Analysis]
10 Chauhan BC, Vianna JR, Sharpe GP, Demirel S, Girkin CA, Mardin CY, Scheuerle AF, Burgoyne CF. Differential Effects of Aging in the Macular Retinal Layers, Neuroretinal Rim, and Peripapillary Retinal Nerve Fiber Layer. Ophthalmology 2020;127:177-85. [PMID: 31668716 DOI: 10.1016/j.ophtha.2019.09.013] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
11 Wilsey LJ, Reynaud J, Cull G, Burgoyne CF, Fortune B. Macular Structure and Function in Nonhuman Primate Experimental Glaucoma. Invest Ophthalmol Vis Sci 2016;57:1892-900. [PMID: 27082305 DOI: 10.1167/iovs.15-18119] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 2.3] [Reference Citation Analysis]
12 Yabana T, Shiga Y, Kawasaki R, Omodaka K, Takahashi H, Kimura K, Togashi K, Horii T, Sasaki K, Yuasa T, Nakazawa T. Evaluating retinal vessel diameter with optical coherence tomography in normal-tension glaucoma patients. Jpn J Ophthalmol 2017;61:378-87. [PMID: 28667424 DOI: 10.1007/s10384-017-0523-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.2] [Reference Citation Analysis]
13 Fortune B, Hardin C, Reynaud J, Cull G, Yang H, Wang L, Burgoyne CF. Comparing Optic Nerve Head Rim Width, Rim Area, and Peripapillary Retinal Nerve Fiber Layer Thickness to Axon Count in Experimental Glaucoma. Invest Ophthalmol Vis Sci 2016;57:OCT404-12. [PMID: 27409499 DOI: 10.1167/iovs.15-18667] [Cited by in Crossref: 18] [Cited by in F6Publishing: 21] [Article Influence: 3.6] [Reference Citation Analysis]
14 Mansberger SL, Menda SA, Fortune BA, Gardiner SK, Demirel S. Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma. Am J Ophthalmol 2017;174:1-8. [PMID: 27818206 DOI: 10.1016/j.ajo.2016.10.020] [Cited by in Crossref: 43] [Cited by in F6Publishing: 40] [Article Influence: 7.2] [Reference Citation Analysis]
15 Raza AS, Hood DC. Evaluation of the Structure-Function Relationship in Glaucoma Using a Novel Method for Estimating the Number of Retinal Ganglion Cells in the Human Retina. Invest Ophthalmol Vis Sci 2015;56:5548-56. [PMID: 26305526 DOI: 10.1167/iovs.14-16366] [Cited by in Crossref: 34] [Cited by in F6Publishing: 29] [Article Influence: 4.9] [Reference Citation Analysis]
16 Bhaduri B, Nolan RM, Shelton RL, Pilutti LA, Motl RW, Moss HE, Pula JH, Boppart SA. Detection of retinal blood vessel changes in multiple sclerosis with optical coherence tomography. Biomed Opt Express 2016;7:2321-30. [PMID: 27375947 DOI: 10.1364/BOE.7.002321] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
17 Medeiros FA, Jammal AA, Mariottoni EB. Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning. Ophthalmology 2021;128:383-92. [PMID: 32735906 DOI: 10.1016/j.ophtha.2020.07.045] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]