Advertisement

Retinal Imaging in Early Alzheimer’s Disease

  • Tom MacGillivray
  • Sarah McGrory
  • Tom Pearson
  • James Cameron
Protocol
Part of the Neuromethods book series (NM, volume 137)

Abstract

Changes in the brain that lead to Alzheimer’s disease are thought to start decades before cognitive symptoms emerge. If biomarkers for these early stages could be identified, it would contribute to a more accurate estimation of an individual’s risk of developing disease and enable the monitoring of high-risk (presymptomatic) persons as well as providing the means for assessing the efficacy of new interventions. The retina links to the visual processing and cognitive centers of the brain, but it is also an extension of the brain sharing embryological origins as well as a blood supply and nerve tissue. It therefore has huge potential as a site for biomarker investigation through easy, noninvasive imaging and computational image analysis to reveal valuable information about microvascular health, deposition, and neurodegenerative damage. Capturing reliable longitudinal data pertaining to the onset of Alzheimer’s disease is a key target, but a high degree of standardization is necessary if the potential of the retina is to be fully realized. Our goal is to provide the reader with guidelines on how to execute robust retinal imaging and analysis for neuroretinal biomarker discovery and to highlight advantages and limitations of the techniques.

Key words

Alzheimer’s disease Dementia Retinal imaging Non-invasive Biomarker Fundus Blood vessel Neurodegeneration 

Notes

Acknowledgments

Support from the Engineering and Physical Sciences Research Council (EPSRC) (grant number EP/M005976/1), the Medical Research Council (MRC) (grant number MR/L015994/1), the Alzheimer’s Research UK Scotland Network Centre, the University of Edinburgh Innovation Initiative Grants scheme, the Edinburgh and Lothians Health Foundation, Optos, and SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence) is gratefully acknowledged. We also thank the Computer Vision and Image Processing Group at the University of Dundee, NHS Lothian R&D, the Edinburgh Clinical Research Facility, Edinburgh Imaging, and the Anne Rowling Regenerative Neurology Clinic.

References

  1. 1.
    London A, Benhar I, Schwartz M (2013) The retina as a window to the brain-from eye research to CNS disorders. Nat Rev Neurol 9(1):44–53.  https://doi.org/10.1038/nrneurol.2012.227 CrossRefPubMedGoogle Scholar
  2. 2.
    Knopman DS (2006) Dementia and cerebrovascular disease. Mayo Clin Proc 81(2):223–230.  https://doi.org/10.4065/81.2.223 CrossRefPubMedGoogle Scholar
  3. 3.
    Reitz C, Brayne C, Mayeux R (2011) Epidemiology of Alzheimer disease. Nat Rev Neurol 7(3):137–152.  https://doi.org/10.1038/nrneurol.2011.2 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Patton N, Aslam T, Macgillivray T et al (2005) Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat 206(4):319–348.  https://doi.org/10.1111/j.1469-7580.2005.00395.x CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    McGrory S, Cameron JR, Pellegrini E et al (2017) The application of retinal fundus camera imaging in dementia: a systematic review. Alzheimers Dement (Amst) 6:91–107.  https://doi.org/10.1016/j.dadm.2016.11.001 Google Scholar
  6. 6.
    Ratnayaka JA, Serpell LC, Lotery AJ (2015) Dementia of the eye: the role of amyloid beta in retinal degeneration. Eye (Lond) 29(8):1013–1026.  https://doi.org/10.1038/eye.2015.100 CrossRefGoogle Scholar
  7. 7.
    Koronyo-Hamaoui M, Koronyo Y, Ljubimov AV et al (2011) Identification of amyloid plaques in retinas from Alzheimer’s patients and noninvasive in vivo optical imaging of retinal plaques in a mouse model. Neuroimage 54(Suppl 1):S204–S217.  https://doi.org/10.1016/j.neuroimage.2010.06.020 CrossRefPubMedGoogle Scholar
  8. 8.
    Anderson DH, Talaga KC, Rivest AJ et al (2004) Characterization of beta amyloid assemblies in drusen: the deposits associated with aging and age-related macular degeneration. Exp Eye Res 78(2):243–256CrossRefPubMedGoogle Scholar
  9. 9.
    Aslam A, Peto T, Barzegar-Befroei N et al (2014) Assessing peripheral retinal drusen progression in Alzheimer’s dementia: a pilot study using ultra-wide field imaging. Invest Ophthalmol Vis Sci 55(13):659–659Google Scholar
  10. 10.
    Thomson KL, Yeo JM, Waddell B et al (2015) A systematic review and meta-analysis of retinal nerve fiber layer change in dementia, using optical coherence tomography. Alzheimers Dement (Amst) 1(2):136–143.  https://doi.org/10.1016/j.dadm.2015.03.001 Google Scholar
  11. 11.
    Cunha LP, Almeida AL, Costa-Cunha LV et al (2016) The role of optical coherence tomography in Alzheimer’s disease. Int J Retina Vitreous 2:24.  https://doi.org/10.1186/s40942-016-0049-4 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Lisboa R, Paranhos A Jr, Weinreb RN et al (2013) Comparison of different spectral domain OCT scanning protocols for diagnosing preperimetric glaucoma. Invest Ophthalmol Vis Sci 54(5):3417–3425.  https://doi.org/10.1167/iovs.13-11676 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    de Carlo TE, Romano A, Waheed NK et al (2015) A review of optical coherence tomography angiography (OCTA). Int J Retina Vitreous 1:5.  https://doi.org/10.1186/s40942-015-0005-8 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Patton N, Aslam TM, MacGillivray T et al (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25(1):99–127.  https://doi.org/10.1016/j.preteyeres.2005.07.001 CrossRefPubMedGoogle Scholar
  15. 15.
    Abramoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Trans Med Imaging 3:169–208.  https://doi.org/10.1109/RBME.2010.2084567 Google Scholar
  16. 16.
    MacGillivray T (2012) VAMPIRE: vessel assessment and measurement platform for images of the retina. In: Tan EYKNJH, Acharya UR, Suri JS (eds) Human eye imaging and modeling. CRC Press, Boca Raton, FLGoogle Scholar
  17. 17.
    Cameron JR, Ballerini L, Langan C et al (2016) Modulation of retinal image vasculature analysis to extend utility and provide secondary value from optical coherence tomography imaging. J Med Imaging (Bellingham) 3(2):020501.  https://doi.org/10.1117/1.JMI.3.2.020501 CrossRefGoogle Scholar
  18. 18.
    Pellegrini E, Robertson G, Trucco E et al (2014) Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy. Biomed Opt Express 5(12):4329–4337.  https://doi.org/10.1364/BOE.5.004329 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Trucco E, Giachetti A, Ballerini L et al. (2015) Morphometric measurements of the retinal vasculature in fundus images with vampire. In: Biomedical image understanding. John Wiley & Sons, Inc., pp 91–111. doi: https://doi.org/10.1002/9781118715321.ch3
  20. 20.
    Knudtson MD, Lee KE, Hubbard LD et al (2003) Revised formulas for summarizing retinal vessel diameters. Curr Eye Res 27(3):143–149CrossRefPubMedGoogle Scholar
  21. 21.
    Lisowska A, Annunziata R, Loh GK et al (2014) An experimental assessment of five indices of retinal vessel tortuosity with the RET-TORT public dataset. Conf Proc IEEE Eng Med Biol Soc 2014:5414–5417.  https://doi.org/10.1109/EMBC.2014.6944850 PubMedGoogle Scholar
  22. 22.
    Doubal FN, MacGillivray TJ, Patton N et al (2010) Fractal analysis of retinal vessels suggests that a distinct vasculopathy causes lacunar stroke. Neurology 74(14):1102–1107.  https://doi.org/10.1212/WNL.0b013e3181d7d8b4 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Stosic T, Stosic BD (2006) Multifractal analysis of human retinal vessels. IEEE Trans Med Imaging 25(8):1101–1107CrossRefPubMedGoogle Scholar
  24. 24.
    Cameron JR, Megaw RD, Tatham AJ et al (2017) Lateral thinking – interocular symmetry and asymmetry in neurovascular patterning, in health and disease. Prog Retin Eye Res.  https://doi.org/10.1016/j.preteyeres.2017.04.003
  25. 25.
    Croft DE, van Hemert J, Wykoff CC et al (2014) Precise montaging and metric quantification of retinal surface area from ultra-widefield fundus photography and fluorescein angiography. Ophthal Surg Lasers Imaging Retina 45(4):312–317.  https://doi.org/10.3928/23258160-20140709-07 CrossRefGoogle Scholar
  26. 26.
    Kolb H (1995) Simple anatomy of the retina. In: Kolb H, Fernandez E, Nelson R (eds) Webvision: the organization of the retina and visual system. University of Utah Health, Salt Lake City, UTGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Tom MacGillivray
    • 1
  • Sarah McGrory
    • 2
  • Tom Pearson
    • 1
  • James Cameron
    • 1
  1. 1.Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
  2. 2.Centre for Cognitive Ageing and Cognitive EpidemiologyUniversity of EdinburghEdinburghUK

Personalised recommendations