Current Medical Science

, Volume 38, Issue 6, pp 968–975 | Cite as

Research Progress in MRI of the Visual Pathway in Diabetic Retinopathy

  • Yu-min Li
  • Hong-mei Zhou
  • Xiang-yang XuEmail author
  • He-shui ShiEmail author


With an increasing incidence, diabetic retinopathy is one of the most important complications of diabetes mellitus (DM) and is also known as one of the major reasons of adult acquired blindness. It is widely accepted that the visual impairment of diabetic patients results from retinal microvascular changes. However, recent clinical experimental and neuroimaging studies suggest that the visual impairment of diabetic patients is also related to the pathophysiological changes of different parts of the visual pathway in diabetic retinopathy. Therefore, the magnetic resonance imaging (MRI) techniques have been widely used for evaluating the microstructural changes, white matter integrity, metabolite changes, and the whole or partial functional and anatomic changes in the diabetic retinopathy patients’ brains in order to fully understand the mechanism of vision loss of the diabetic retinopathy patients. This review focuses on the research progress in application of MRI of the visual pathway in diabetic retinopathy.

Key words

diabetic retinopathy visual pathway visual impairment magnetic resonance imaging 


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  1. 1.
    Calderon GD, Juarez OH, Hernandez GE, et al. Oxidative stress and diabetic retinopathy: development and treatment. Eye, 2017,64(4):1–9Google Scholar
  2. 2.
    Harris Nwanyanwu K, Talwar N, Gardner TW, et al. Predicting development of proliferative diabetic retinopathy. Diabetes Care, 2013,36(6):1562–1568Google Scholar
  3. 3.
    Leske MC, Wu SY, Hennis A, et al. Hyperglycemia, blood pressure, and the 9-yearincidence of diabetic retinopathy: the Barbados Eye Studies. Ophthalmol, 2005,112(5):799–805Google Scholar
  4. 4.
    Chew EY, Davis MD, Danis RP, et al. The effects of medical management on the progression of diabetic retinopathy in persons with type 2 diabetes: the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Eye Study. Ophthalmol, 2014,121(12):2443–2451Google Scholar
  5. 5.
    Estacio RO, McFarling E, Biggerstaff S, et al. Overt albuminuria predicts diabetic retinopathy in Hispanics with NIDDM. Am J Kidney Dis, 1998,31(6):947–953Google Scholar
  6. 6.
    Wolfensberger TJ, Hamilton AM. Diabetic retinopathy—an historical review. Semin Ophthalmol, 2001,16(1):2–7Google Scholar
  7. 7.
    Saaddine JB, Honeycutt AA, Narayan KM, et al. Projection of diabetic retinopathy and other major eye diseases among people with diabetes mellitus: United States, 2005–2050. Arch Ophthalmol, 2008,126(12):1740–1747Google Scholar
  8. 8.
    Ho LC, Wang B, Conner IP, et al. In Vivo Evaluation of White Matter Integrity and Anterograde Transport in Visual System After Excitotoxic Retinal Injury With Multimodal MRI and OCT. Invest Ophthalmol Vis Sci, 2015,56(6):3788–3800Google Scholar
  9. 9.
    Kancherla S, Kohler WJ, der Merwe Y, et al. In Vivo Evaluation of the Visual Pathway in Streptozotocin-Induced Diabetes by Diffusion Tensor MRI and Contrast Enhanced MR. PLoS One, 2016,11(10):1–15Google Scholar
  10. 10.
    Roy S, Amin S, Roy S. Retinal fibrosis in diabetic retinopathy. Exp Eye Res, 2016,142(1):71–75Google Scholar
  11. 11.
    Shi Y, Hu FB. The global implications of diabetes and cancer. Lancet, 2014,383(9933):1947–1958Google Scholar
  12. 12.
    Sun JK, Radwan SH, Soliman AZ, et al. Neural retinal disorganization as a robust marker of visual acuity in current and resolved diabetic macular edema. Diabetes, 2015,64(7):2560–2570Google Scholar
  13. 13.
    Klein R, Lee KE, Gangnon RE, et al. The 25-year incidence of visual impairment in type 1 diabetes mellitus the Wisconsin Epidemiologic Study of Diabetic Retinopathy. Ophthalmol, 2010,117(1):6370Google Scholar
  14. 14.
    Ewing FM, Deary IJ, Strachan MW, et al. Seeing beyond retinopathy in diabetes: electrophysiological and psychophysical abnormalities and alterations invision. Endoce Rev, 1998,19(4):462–476Google Scholar
  15. 15.
    Karlica D, Galetovic D, Ivanisevic M, et al. Visual evoked potential can be used to detect a prediabetic form of diabetic retinopathy in patients with diabetes mellitus type I. Coll Antropol, 2010,34(2):525–529Google Scholar
  16. 16.
    Wolff BE, Bearse MA Jr, Schneck ME, et al. Multifocal VEP (mfVEP) reveals abnormal neuronal delays in diabetes. Doc ophthalmol, 2010,121(3):189–196Google Scholar
  17. 17.
    Yamazaki H, Adachi-Usami E, Chiba J. Contrast thresholds of diabetic patients determined by VECP and psychophysical measurements. Acta Endocrinol (Copenh), 1982,60(3):386–392Google Scholar
  18. 18.
    Fernandez DC, Pasquini LA, Dorfman D, et al. Early distal axonopathy of the visual pathway in experimental diabetes. AM J Pathol, 2012,180(1):303–313Google Scholar
  19. 19.
    Chelsea SK, Jeffry RA, Jeffrey L, et al. Beyond mismatch: evolving paradigms in imaging the ischemic penumbra with multimodal magnetic resonance imaging. Stroke, 2003,34(11):2729–2735Google Scholar
  20. 20.
    Patton N, Aslam T, Macgillivray T, et al. Retinal vascular image analyses as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat, 2005,206(4):319–348Google Scholar
  21. 21.
    Wong TY, Mosley TH, Klein R, et al. Retinal microvascular changes and MRI signs of cerebral atrophy in healthy, middle-aged people. Neurology, 2003,61(6):806–811Google Scholar
  22. 22.
    Biessels GJ, Reijmer YD. Brain changes underlying cognitive dysfunction in diabetes: what can we learn from MRI? Diabetes, 2014,63(7):2244–2252Google Scholar
  23. 23.
    Gupta N, Ang LC, Noel de Tilly L, et al. Human glaucoma and neural degeneration in intracranial optic nerve, later geniculate nucleus, and visual cortex. Brit J Ophthalmol, 2006,90(6):674–678Google Scholar
  24. 24.
    Ptito M, Schneider FC, Paulson OB, et al. Alterations of the visual pathways in congenital blindness. Exp Brain Res, 2008,187(1):41–49Google Scholar
  25. 25.
    Huge Schmidt CE, Lovato JF, Ambrosius WT, et al. The cross-sectional and longitudinal associations of diabetic retinopathy with cognitive function and brain MRI findings: the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Diabetes Care, 2014,37(12):3244–3252Google Scholar
  26. 26.
    Wessels AM, Simsek S, Remijnse PL, et al. Voxel-based morphometry demonstrates reduced grey matter density on brain MRI in patients with diabetic retinopathy. Diabetolog, 2006,49(10):2474–2480Google Scholar
  27. 27.
    Brands AM, Biessels GJ, de Haan EH, et al. The effects of type 1 diabetes on cognitive performance: a metaanalysis. Diabetes Care, 2005,28(3):726–735Google Scholar
  28. 28.
    Ryan CM, Geckle MO, Orchard TJ. Cognitive efficiency declines over time in adults with Type 1 diabetes: effects of micro-and macrovascular complications. Diabetolog, 2003,46(7):940–948Google Scholar
  29. 29.
    Antonetti DA, Klein R, Gardner TW, et al. Diabetic retinopathy. N Engl J Med, 2012,366(13):1227–1239Google Scholar
  30. 30.
    Kollias AN, Ulbig MW. Diabetic retinopathy early diagnosis and effective treatment. Dtsch Arztebl Int, 2010,107(5):75–84Google Scholar
  31. 31.
    Berkowitz BA, Roberts R, Luan H, et al. Dynamic contrast-enhanced MRI measurements of passive permeability through blood retinal barrier in diabetic rats. Invest Ophthalmol Vis Sci, 2004,45(7):2391–2398Google Scholar
  32. 32.
    Watanabe T, Michaelis T, Frahm J. Mapping of retinal projections in the living rat using high-resolution 3D gradient-echo MRI with Mn2+-induced contrast. Magne Reson Med, 2001,46(3):424–429Google Scholar
  33. 33.
    Berkowitz BA, Roberts R, Stemmler A, et al. Impaired Apparent Ion Demand in Experimental Diabetic Retinopathy: Correction by Lipoic Acid. Invest ophth Vis Sci, 2007,48(10):4753–4758Google Scholar
  34. 34.
    Modi S, Bhattacharya M, Sekhri T, et al. Assessment of the metabolic profile in type 2 diabetes mellitus and hypothyroidism through proton MR spectroscopy. J Magn Reson Imaging, 2008.26(3):420–425Google Scholar
  35. 35.
    Ozsoy E, Doganay S, Dogan M, et al. Ealuation of metabolite changes in visual cortex in diabetic retinopathy by MR-Spectroscopy. J Diabetes Complications, 2012,26(3):241–245Google Scholar
  36. 36.
    Berkowitz BA, Bansal N, Wilson CA. Non-invasive measurement of steady-state vitreous lactate concentration. NMR Biomed, 1994,7(6):263–268Google Scholar
  37. 37.
    Rucker JC, Biousse V, Mao H, et al. Detection of lactate in the human vitreous body using proton magnetic resonance spectroscopy. Arch Ophtaimol, 2003,121(6):909–911Google Scholar
  38. 38.
    Sahin I, Alkan A, Keskin L, et al. Evaluation of in vivo cerebral metabolism on proton magnetic resonance spectroscopy in patients with impaired glucose tolerance and type 2 diabetes mellitus. J Diabetes Complications, 2008,22(4):254–260Google Scholar
  39. 39.
    Kitajima M, Korogi Y, Hirai T, et al. MR changes in the calcarine area resulting from retinal degeneration. AJNR, 1997,18(7):1291–1295Google Scholar
  40. 40.
    Dogan M, Ozsoy E, Doganay S, et al. Brain diffusionweighted imaging in diabetic patients with retinopathy. Eur Rev Med Pharmacol Sci, 2012,16(1):126–131Google Scholar
  41. 41.
    Liang M, Chen X, Xue F, et al. Diffusion-weighted imaging of injuries to the visual centers of the brain in patients with type 2 diabetes and retinopathy. Exp Ther Med, 2017,14(2):1153–1156Google Scholar
  42. 42.
    Wang Z, Lu Z, Li J, et al. Evaluation of apparent diffusion coefficient measurements of brain injury in type 2 diabetics with retinopathy by diffusion-weighted MRI at 3.0 T. Neuroreport, 2017,28(2):69–74Google Scholar
  43. 43.
    Krabbe K, Gideon P, Wagn P, et al. MR diffusion imaging of human intracranial tumours. Neuroradiology, 1997,39(7):483–489Google Scholar
  44. 44.
    Stahl R, Dietrich O, Teipel SJ, et al. White matter damage in Alzheimer disease and mild cognitive impairment: assessment with Diffusion-tensor MR imaging and parallel imaging techniques. Radiology, 2007,243(2):483–492Google Scholar
  45. 45.
    Hajiabadi M, Samii M, Fahlbusch R, et al. A preliminary study of the clinical application of optic pathway diffusion tensor tractography in suprasellar tumor surgery: preoperative, intraoperative, and postoperative assessment. Neurosurg, 2016,125(3):759–765Google Scholar
  46. 46.
    Sun SW, Liang HF, Le TQ, et al. Differential sensitivity of in vivo and ex vivo diffusion tensor imaging to evolving optic nerve injury in mice with retinal ischemia. Neuroimage, 2006,32(3):1195–1204Google Scholar
  47. 47.
    Song SK, Sun SW, Ju WK, et al. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage, 2003,20(3):1714–1722Google Scholar
  48. 48.
    Kodl CT, Franc DT, Rao JP, et al. Diffusion tensor imaging identifies deficits in white matter microstructure in subjects with type 1 diabetes that correlate with reduced neurocognitive function. Diabetes, 2008,57(6):3083–3089Google Scholar
  49. 49.
    Franc DT, Kodl CT, Mueller BA, et al. High connectivity between reduced cortical thickness and disrupted white matter tracts in long-standing type 1 diabetes. Diabetes, 2011, 60(1)315–319Google Scholar
  50. 50.
    Rong W, Yu ZF, Wei TJ, et al. Evaluation of changes in magnetic resonance diffusion tensor imaging of the bilateral optic tract in monocular blind rats. Int J Dev Neurosci, 2017,59(2):10–14Google Scholar
  51. 51.
    Zi CQ, Ping N, Yu LN, et al. Visual Pathway Lesion and Its Development During Hyperbaric Oxygen Treatment: A Bold fMRI and DTI Study. J Magn Reson Imaging, 2010,31(5):1054–1060Google Scholar
  52. 52.
    Cui Y, Jiao Y, Chen YC, et al. Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study. Diabetes, 2014,63(2):749–760Google Scholar
  53. 53.
    Anurova I, Renier LA, De Volder AG, et al. Relationship between cortical thickness and functional activation in the early blind. Cereb Cortex, 2015,25(8):2035–2048Google Scholar
  54. 54.
    van Duinkerken E, Schoonheim MM, Sanz-Arigita EJ, et al. Resting-state brain networks in type 1 diabetic patients with and without microangiopathy and their relation to cognitive functions and disease variables. Diabetes, 2012,61(7):1814–1821Google Scholar

Copyright information

© Huazhong University of Science and Technology 2018

Authors and Affiliations

  1. 1.Department of Radiology, Liyuan Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of Radiology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina

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