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      Non-invasive in vivo hyperspectral imaging of the retina for potential biomarker use in Alzheimer’s disease

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      1 , 2 , , 1 , 2 , 3 , 4 , 1 , 2 , 1 , 2 , 1 , 2 , 5 , 6 , 7 , 4 , 8 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 12 , 17 , 18 , 12 , 17 , 18 , 19 , 20 , 20 , 21 , 1 , 2 , 22 , 3 , 3 , 23 , 1 , 2 , 1 , 2 ,
      Nature Communications
      Nature Publishing Group UK
      Alzheimer's disease, Biomarkers

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          Abstract

          Studies of rodent models of Alzheimer’s disease (AD) and of human tissues suggest that the retinal changes that occur in AD, including the accumulation of amyloid beta (Aβ), may serve as surrogate markers of brain Aβ levels. As Aβ has a wavelength-dependent effect on light scatter, we investigate the potential for in vivo retinal hyperspectral imaging to serve as a biomarker of brain Aβ. Significant differences in the retinal reflectance spectra are found between individuals with high Aβ burden on brain PET imaging and mild cognitive impairment ( n = 15), and age-matched PET-negative controls ( n = 20). Retinal imaging scores are correlated with brain Aβ loads. The findings are validated in an independent cohort, using a second hyperspectral camera. A similar spectral difference is found between control and 5xFAD transgenic mice that accumulate Aβ in the brain and retina. These findings indicate that retinal hyperspectral imaging may predict brain Aβ load.

          Abstract

          The use of PET for detection of Aβ in the brain in AD has limitations; studies also indicate that retinal changes, including Aβ deposition, occur in AD. Here the authors demonstrate the potential to use in vivo retinal hyperspectral imaging as a surrogate for brain accumulation of Aβ.

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          Most cited references36

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          Theory of Edge Detection

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            Neuron loss in the 5XFAD mouse model of Alzheimer’s disease correlates with intraneuronal Aβ42 accumulation and Caspase-3 activation

            Background Although the mechanism of neuron loss in Alzheimer’s disease (AD) is enigmatic, it is associated with cerebral accumulation of Aβ42. The 5XFAD mouse model of amyloid deposition expresses five familial AD (FAD) mutations that are additive in driving Aβ42 overproduction. 5XFAD mice exhibit intraneuronal Aβ42 accumulation at 1.5 months, amyloid deposition at 2 months, and memory deficits by 4 months of age. Results Here, we demonstrate by unbiased stereology that statistically significant neuron loss occurs by 9 months of age in 5XFAD mice. We validated two Aβ42-selective antibodies by immunostaining 5XFAD; BACE1−/− bigenic brain sections and then used these antibodies to show that intraneuronal Aβ42 and amyloid deposition develop in the same regions where neuron loss is observed in 5XFAD brain. In 5XFAD neuronal soma, intraneuronal Aβ42 accumulates in puncta that co-label for Transferrin receptor and LAMP-1, indicating endosomal and lysosomal localization, respectively. In addition, in young 5XFAD brains, we observed activated Caspase-3 in the soma and proximal dendrites of intraneuronal Aβ42-labeled neurons. In older 5XFAD brains, we found activated Caspase-3-positive punctate accumulations that co-localize with the neuronal marker class III β-tubulin, suggesting neuron loss by apoptosis. Conclusions Together, our results indicate a temporal sequence of intraneuronal Aβ42 accumulation, Caspase-3 activation, and neuron loss that implies a potential apoptotic mechanism of neuron death in the 5XFAD mouse.
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              Blood vessel segmentation methodologies in retinal images--a survey.

              Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
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                Author and article information

                Contributors
                xavier.hadoux@unimelb.edu.au
                peterv@unimelb.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 September 2019
                17 September 2019
                2019
                : 10
                : 4227
                Affiliations
                [1 ]GRID grid.410670.4, Centre for Eye Research Australia, , Royal Victorian Eye and Ear Hospital, ; East Melbourne, 3002 VIC Australia
                [2 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Ophthalmology, Department of Surgery, , University of Melbourne, ; Parkville, 3010 VIC Australia
                [3 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Optometry and Vision Sciences, , University of Melbourne, ; Parkville, 3010 VIC Australia
                [4 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, The Florey Institute, , The University of Melbourne, ; Parkville, 3010 VIC Australia
                [5 ]ISNI 0000 0004 1936 7857, GRID grid.1002.3, Faculty of Medicine, Nursing and Health Sciences, , Monash University, ; Clayton, 3800 VIC Australia
                [6 ]ISNI 0000 0004 0624 1200, GRID grid.416153.4, Neuropsychiatry Unit, North Western Mental Health, Melbourne Health, Royal Melbourne Hospital, ; Parkville, 3050 VIC Australia
                [7 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, University of Melbourne, Department of Psychiatry, ; Parkville, 3010 VIC Australia
                [8 ]GRID grid.410678.c, Austin Health, ; Melbourne, 3084 VIC Australia
                [9 ]ISNI 0000 0004 0409 2862, GRID grid.1027.4, Centre for Astrophysics and Supercomputing, , Swinburne University of Technology, ; Melbourne, 3122 VIC Australia
                [10 ]ISNI 0000 0004 0409 2862, GRID grid.1027.4, OzGrav-Swinburne, Centre for Astrophysics & Supercomputing, , Swinburne University of Technology, ; Melbourne, 3122 VIC Australia
                [11 ]ISNI 0000 0004 0409 2862, GRID grid.1027.4, Advanced Visualisation Laboratory, Digital Research Innovation Capability Platform, , Swinburne University of Technology, ; Melbourne, 3122 VIC Australia
                [12 ]ISNI 0000 0004 1936 8649, GRID grid.14709.3b, McConnell Brain Imaging Centre, Montreal Neurological Institute, , McGill University, ; Montreal, H3A 2B4 QC Canada
                [13 ]ISNI 0000 0004 1936 8630, GRID grid.410319.e, PERFORM Centre, , Concordia University, ; Montreal, H4B 1R6 QC Canada
                [14 ]École Polytechnique de Montréal, Institut de génie biomédical, Département de Génie électrique, Montreal, H3C 3A7 QC Canada
                [15 ]Research Center, Montreal Heart Institute, Montreal, H1T 1C8 QC Canada
                [16 ]GRID grid.474038.c, Optina Diagnostics, ; Montreal, H4T 1Z2 QC Canada
                [17 ]ISNI 0000 0001 2353 5268, GRID grid.412078.8, Translational Neuroimaging Laboratory, McGill Centre for Studies in Aging, , Douglas Mental Health University Institute, ; Montreal, H4H 1R3 QC Canada
                [18 ]ISNI 0000 0004 1936 8649, GRID grid.14709.3b, Alzheimer’s Disease Research Unit, , The McGill University Research Centre for Studies in Aging, McGill University, ; Montreal, H4H 1R3 QC Canada
                [19 ]MoCA Clinic and Institute, Greenfield Park, J4V 2J2 QC Canada
                [20 ]Clinique ophtalmologique 2121, Montreal, H3H 1G6 QC Canada
                [21 ]ISNI 0000 0001 0742 1666, GRID grid.414216.4, Département de médecine nucléaire, , Hôpital Maisonneuve-Rosemont, ; Montreal, H1T 2M4 QC Canada
                [22 ]ISNI 0000 0000 9960 1711, GRID grid.419272.b, Singapore Eye Research Institute, , Singapore National Eye Centre, ; Singapore, 169856 Singapore
                [23 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Murdoch Children’s Research Institute and Department of Paediatrics, , University of Melbourne, ; Melbourne, 3052 VIC Australia
                Author information
                http://orcid.org/0000-0002-4524-3706
                http://orcid.org/0000-0003-2218-9951
                http://orcid.org/0000-0002-4694-9479
                http://orcid.org/0000-0002-7408-9453
                http://orcid.org/0000-0002-4953-4500
                http://orcid.org/0000-0003-0961-2321
                http://orcid.org/0000-0001-9116-1376
                http://orcid.org/0000-0002-3298-3086
                Article
                12242
                10.1038/s41467-019-12242-1
                6748929
                31530809
                8eed1fdf-1707-4e4b-9e53-166f81bcddf0
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 November 2018
                : 27 August 2019
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                © The Author(s) 2019

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                alzheimer's disease,biomarkers
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                alzheimer's disease, biomarkers

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