Affiliations: [a]
Alzheimer’s Research Australia, Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, WA, Australia
| [b]
Centre of Excellence for Alzheimer’s Disease Research and Care, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| [c]
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia
| [d]
Australian e-Health Research Centre, Floreat, WA, Australia
| [e]
Lifespan Health and Wellbeing Research Centre, Macquarie Medical School, Macquarie University, Macquarie Park, NSW, Australia
| [f] Lions Alzheimer’s Foundation, Perth, WA, Australia
| [g]
Centre for Healthy Ageing, Health Futures Institute, Murdoch University, Perth, WA, Australia
Correspondence:
[*]
Correspondence to: Eugene Hone, C/- Level 2, Ralph and Patricia Sarich Neuroscience Research Institute, QEII Medical Centre (RR block), 8 Verdun Street, Nedlands, Perth, Western Australia 6009. E-mail: e.hone@ecu.edu.au.
Abstract: Background:As an extension of the central nervous system (CNS), the retina shares many similarities with the brain and can manifest signs of various neurological diseases, including Alzheimer’s disease (AD). Objective:To investigate the retinal spectral features and develop a classification model to differentiate individuals with different brain amyloid levels. Methods:Sixty-six participants with varying brain amyloid-β protein levels were non-invasively imaged using a hyperspectral retinal camera in the wavelength range of 450–900 nm in 5 nm steps. Multiple retina features from the central and superior views were selected and analyzed to identify their variability among individuals with different brain amyloid loads. Results:The retinal reflectance spectra in the 450–585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. A classification model was trained to differentiate individuals with varying amyloid levels using the spectra of extracted retinal features. The performance of the spectral classification model was dependent upon retinal features and showed 0.758–0.879 accuracy, 0.718–0.909 sensitivity, 0.764–0.912 specificity, and 0.745–0.891 area under curve for the right eye. Conclusions:This study highlights the spectral variation of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging technique as a potential method to identify individuals in the preclinical phase of AD as an inexpensive alternative to brain imaging.
