Imaging Amyloid and Tau in the Retina: Current Research and Future Directions

The retina is a key focus in the search for biomarkers of Alzheimer's disease (AD) because of its accessibility and shared development with the brain. The pathological hallmarks of AD, amyloid beta (Ab), and hyper-phosphorylated tau (pTau) have been identified in the retina, although histopathologic findings have been mixed. Several imaging-based approaches have been developed to detect retinal AD pathology in vivo. Here, we review the research related to imaging AD-related pathology in the retina and implications for future biomarker research.

State-of-the-Art Review Section Editors: Fiona Costello, MD, FRCP(C) Sashank Prasad, MD Imaging Amyloid and Tau in the Retina: Current Research and Future Directions Mira Y. Tang, Marian S. Blazes, MD, Cecilia S. Lee, MD, MS Background: The retina is a key focus in the search for biomarkers of Alzheimer’s disease (AD) because of its accessibility and shared development with the brain. The pathological hallmarks of AD, amyloid beta (Ab), and hyperphosphorylated tau (pTau) have been identified in the retina, although histopathologic findings have been mixed. Several imaging-based approaches have been developed to detect retinal AD pathology in vivo. Here, we review the research related to imaging AD-related pathology in the retina and implications for future biomarker research. Evidence Acquisition: Electronic searches of published literature were conducted using PubMed and Google Scholar. Results: Curcumin fluorescence and hyperspectral imaging are both promising methods for detecting retinal Ab, although both require validation in larger cohorts. Challenges remain in distinguishing curcumin-labeled Ab from background fluorescence and standardization of dosing and quantification methods. Hyperspectral imaging is limited by confounding signals from other retinal features and variability in reflectance spectra between individuals. To date, evidence of tau aggregation in the retina is limited to histopathologic studies. New avenues of research are on the horizon, including near-infrared fluorescence imaging, novel Ab labeling techniques, and small molecule retinal tau tracers. Artificial intelligence (AI) approaches, including machine learning models and deep learning-based image analysis, are active areas of investigation. Conclusions: Although the histopathological evidence seems promising, methods for imaging retinal Ab require further validation, and in vivo imaging of retinal tau remains elusive. AI approaches may hold the greatest promise for the discovery of a characteristic retinal imaging profile of AD. Elucidating the role of Ab and pTau in the retina will provide key insights into the complex processes involved in aging and in neurodegenerative disease. Journal of Neuro-Ophthalmology 2023;43:168–179 doi: 10.1097/WNO.0000000000001786 © 2023 by North American Neuro-Ophthalmology Society Wellesley College (MT), Wellesley, Massachusetts; Department of Ophthalmology (MB, CSL), University of Washington, Seattle, Washington; and Roger and Angie Karalis Johnson Retina Center (CSL), Seattle, Washington. Supported by National Institutes of Health grants R01AG060942 and U19AG066567, the Latham Vision Research Innovation Award (Seattle, WA), the Klorfine Family Endowed Chair, and an unrestricted grant from Research to Prevent Blindness. The sponsor or funding organization had no role in the design or conduct of this research. The authors report no conflicts of interest. Address correspondence to Cecilia S. Lee, MD, MS, Department of Ophthalmology, University of Washington, Box 359608, 325 Ninth Avenue, Seattle, WA 98104; E-mail: [e-mail] 168 A lzheimer’s disease (AD) is an irreversible and progressive form of dementia, affecting more than 6 million people in the United States. (1). AD is typically late onset with neuropathological changes occurring years before clinical symptoms appear (2). Currently, the definitive diagnosis still relies on the postmortem identification of pathological hallmarks including extracellular aggregations of amyloid beta (Ab) and intracellular neurofibrillary tangles (NFTs) containing hyperphosphorylated tau (pTau) (3). Cerebrospinal fluid (CSF) analysis and positron emission tomography (PET) imaging can provide information about Ab and pTau burden and are often used as diagnostic criteria but are expensive, invasive (4), and do not always predict cognitive decline in asymptomatic patients (5). A more accessible, noninvasive biomarker is needed to diagnose early stage AD, enabling clinical trial research and potential therapeutic interventions. The retina is a light-sensitive layer of neural tissue with a direct connection to the brain through the optic nerve. The retina develops from the embryonic diencephalon and thus shares many structural and functional features with the brain (4,6). Advances in retinal imaging technology are enabling researchers to explore the retina as a noninvasive source of potential AD biomarkers. Multiple retinal structural changes are associated with AD, including retinal ganglion cell (GC) neurodegeneration, abnormal retinal nerve fiber layer (RNFL) thickness and volume, and optic nerve axonal loss (7). Although findings are conflicting, retinal vascular changes have been also observed, including decreased vascular density, tortuosity, and branching complexity in patients with research criteria-based diagnosis of MCI and AD (8–11). In addition, there is growing evidence that the hallmark pathological signs of AD, Ab and pTau deposition, may occur in the retina of patients with AD (12), potentially providing unique insights into preclinical disease progression. Here, we review the current research related to imaging amyloid/tau pathology in the retina and implications for developing retinal biomarkers of AD. RETINAL AMYLOID Pathological amyloid accumulation in the retina has been linked to aging and neurodegenerative eye and brain disease (13). In AD mouse models, retinal Ab deposits increase with age, correlate with cerebral Ab load (14,15), and have been identified in the retina before the brain (16), Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review suggesting that retinal Ab may be a promising target for early AD detection. Histopathological analysis of human AD retinas has demonstrated a range of Ab findings (17). KoronyoHamaoui et al (16) identified Ab plaques in flat-mount retinas from patients with neuropathologically confirmed AD vs age-matched normal controls (NCs), as well significant correlations between retinal Ab and cerebral amyloid burden (18). La Morgia et al (19) identified Ab deposition inside and around melanopsin retinal ganglion cells (mRGCs) which was associated with mRGC death. By contrast, neither Schön et al nor Ho et al detected Ab when performing immunohistochemical analysis of retinal crosssections from neuropathologically confirmed patients with AD (20,21). A 2016 meta-analysis of retinal Ab histopathology studies found significant statistical heterogeneity between studies, likely due to varied tissue preparation and staining techniques, age and length of disease, diagnosis criteria, and other methodological differences (22). More recent studies have had mixed findings. Lee et al (23) found that greater retinal intracellular Ab correlated with lower cerebral neuritic plaque scores in the temporal region, whereas extracellular retinal Ab was associated with higher cerebral amyloid angiopathy scores. Shi et al (24) found extensive vascular Ab deposition and pericyte loss in the AD retinas, suggesting that retinal Ab pathology may resemble the neuropathology implicated in the blood–brain barrier breakdown in AD. By contrast, den Haan et al performed extensive immunohistochemical analyses of the retinas from 6 neuropathologically confirmed AD cases and found no significant differences between AD and age-matched NCs (25). IN VIVO DETECTION OF Ab PLAQUES IN HUMAN RETINAS Curcumin Fluorescence Curcumin labeling is a technique used to image retinal Ab in vivo. Curcumin is a fluorochrome with a high-binding affinity to Ab, and several formulations have been developed for oral administration before imaging (26). Curcumin fluorescence is captured using scanning laser ophthalmoscopy (SLO) with specific excitation and emission filter settings to distinguish curcumin-labeled Ab from background retinal fluorescence (16). Koronyo et al (18) were the first to successfully implement this technique in 10 patients with mild-to-moderate clinical AD and 6 NC participants, using automated image analysis to assign a retinal amyloid index (RAI) score based on number, area, intensity, and distribution of spots showing increased fluorescence. Patients with AD had significantly higher RAI scores compared with NCs (2.1-fold in the age-matched subgroup analysis, P = 0.0031), although there was no correlation between RAI and MMSE scores. Ab deposits localized to the peripheral superior quadrant and Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 to blood vessels. Dumitrascu et al (27) evaluated curcumin fluorescence in patients with probable AD and MCI and found that higher retinal amyloid count (RAC) and greater total retinal amyloid (RA) area in the proximal midperiphery region of the superior-temporal quadrant correlated with lower cognitive scores and decreased hippocampal volume (Fig. 1). In a subsequent study evaluating retinal vascular parameters associated with AD, they found a significant correlation between impaired cognition and a combined Ab/ venous tortuosity index variable, although there were no differences between cognitively impaired participants and NCs associated with any individual vascular features (28). To evaluate retinal Ab in early stage AD, Ngolab et al (29) performed curcumin imaging on 4 asymptomatic Ab PET+ participants and 4 Ab PET- participants. Although all participants were cognitively normal, the Ab PET- participants had fewer fluorescent spots than the preclinical AD participants (difference in means: 2538, 95% confidence intervals [CI]: 2979, 97), and total RAC correlated positively with cerebral Ab as measured by PET standardized uptake value ratio (SUVr) (Pearson’s r: 0.65). Tadokoro et al evaluated associations between retinal curcumin fluorescence and MRI brain features in 30 participants (13 AD, 7 MCI, 10 NC), finding that RAC correlated with whole gray matter atrophy (r = 0.51, P , 0.05) but not with medial temporal lobe atrophy, suggesting that retinal Ab may be related to visual cortex changes (30). Similar to Koronyo et al, the authors found no correlation between RAC and MMSE scores and noted that retinal Ab tended to localize along blood vessel walls. One of the 3 PET Ab+ participants showed faint Ab fluorescence, suggesting variability in curcumin uptake/ fluorescence between individuals or even differing rates of retinal Ab deposition in biomarker-positive AD. Although the abovementioned studies strongly suggest the presence of retinal Ab in AD and associations between retinal Ab and cognition and/or AD pathology in the brain, den Haan et al (31) found no differences in retinal fluorescent signals between patients with biomarker positive AD and NCs using 3 formulations of oral curcumin. In participants who received the standard formulation (Longvida, Verdure Sciences, Noblesville, IN), quantitative assessment revealed no difference in hyperfluorescence between biomarkerpositive AD participants and NCs. Low plasma levels may prevent curcumin from sufficiently binding to Ab (32), and plasma levels of unconjugated curcumin were undetectable after administration of all 3 formulations, although deconjugation of samples using b-glucuronidase increased curcumin levels for 2 of the 3 formulations (NovaSOL [Molecular Health Technologies LLC, Millburn, NJ] and Theracurmin [Integrative Therapeutics LLC, Green Bay, WI]). Methodological differences in curcumin protocols and quantification may explain the varied findings (Table 1). Ngolab et al (29) theorized that the higher RAC they found compared with Koronyo et al may have been due to higher curcumin dosing over a shorter period, whereas results from 169 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review FIG. 1. Representative fluorescent fundus images and retinal amyloid plaque scores from 4 participants stratified by the Montreal Cognitive Assessment (MoCA) score. A and C. Normal control participants (MoCA . 26). B. Participant with amnestic mild cognitive impairment (MoCa # 26). D. Participant with Alzheimer’s disease. Adapted from Dumitrascu OM, Lyden PD, Torbati T, Sheyn J, Sherzai A, Sherzai D, Sherman DS, Rosenberry R, Cheng S, Johnson KO, Czeszynski AD, Verdooner S, Frautschy S, Black KL, Koronyo Y, Koronyo-Hamaoui M. Sectoral segmentation of retinal amyloid imaging in subjects with cognitive decline. Alzheimers Dement (Amst). 2020;12:e12109. den Haan et al (31) suggest that curcumin uptake may vary. In addition, the retina’s inherent autofluorescence may mask the curcumin signal. The presence of cataract may also impair shorter excitation light waves from reaching the retina (33) (Table 1). Some findings were relatively consistent between studies, particularly localization of retinal Ab to the peripheral superior temporal quadrant, which aligns with histopathology findings. However, all studies excluded patients with glaucoma and AMD, which may show overlapping fluorescence patterns (34). Although all studies were cross-sectional and limited by small sample size, only one study found a correlation between retinal Ab and cognition (27). Further research is needed to assess the value of curcumin-based fluorescent imaging as a reliable approach for quantifying retinal Ab, particularly in light of conflicting results from histopathology studies. Hyperspectral Imaging Hyperspectral (HS) imaging has also been used to identify retinal Ab based on spectral shifts in the scattering of light. The retina is illuminated with varying bandwidths of visible to near infrared light, and reflected light is analyzed to determine reflectance intensity at each wavelength, as well as spatial information based on light scattering at each pixel (35,36). Although HS imaging cannot visualize Ab directly, alterations in the reflected light spectra are believed to be caused by the Rayleigh light–scattering properties of small soluble Ab oligomers. Decreases in reflectance in the wavelength range of 480–550 nm are believed to be specific to Ab (37). Thus, HS imaging is a promising imaging modality that does not require oral administration of a labeling agent. 170 More et al (37) demonstrated the potential of HS imaging for retinal amyloid detection by analyzing low-order Ab aggregates in neuronal cell cultures to develop a characteristic spectral signal. They identified similar signals using in vivo HS imaging of AD mouse retinas before the onset of cognitive decline and in ex vivo human AD retinas (38). In a subsequent study, 15 cognitively impaired individuals and 16 age-matched NCs underwent HS imaging to evaluate differences in optical density signature (derived from the mean spectral profile). The greatest difference between AD and NCs occurred in the 2 AD subgroups with the least cognitive impairment, suggesting that HS imaging may identify retinal changes occurring early in AD (39). Hadoux et al (35) used HS imaging to investigate the relationship between retinal Ab and PET imaging findings in 15 Ab PET+ participants and 20 age-matched Ab PETparticipants. They adjusted for individual spectral variation by correcting for the spectral components most responsible for reflectance differences between subjects, such as ocular media, macular pigment, melanin, and hemoglobin (Fig. 2). They sampled 6 regions, noting that structural features cause reflectance spectra to vary between regions. A linear discrimination machine learning (ML) model analyzed the spectral data and assigned a score based on weighted summation of reflectance wavelengths at each region. HS scores differed significantly between cases and controls for all regions, with the most significant difference in the temporal vascular arcade superior to the fovea (P = 0.002). The model classified a separate cohort of Ab PET+ (n = 4) and Ab PET2 (n = 13) participants, achieving an area under the receiver operating characteristic curve (AUC) of 0.87 (P = 0.03, 95% CI: 0.69, 1.0). Notably, differences in Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 Study Curcumin Studies Koronyo et al (18) Participants -10 mild to moderate clinical AD (mean age=76) -6 NC (mean age=53) Dumitrascu et al (27) -22 amnestic MCI -3 probable AD -1 possible FTD -8 NC Dumitrascu et al (28) -6 amnestic MCI -9 multidomain MCI -2 probable AD -1 possible FTD -11 NC Ngolab et al (29) -4 preclinical AD (Ab PET+, normal cognition) -4 NC (Ab PET2) Tadokoro et al (30) -13 research criteria AD (3 Ab PET+) -7 MCI -10 NC den Haan et al (31) -26 biomarker positive AD (CSF or PET) -14 NC Hyperspectral imaging studies Imaging Methods Curcumin: Longvida*, 2 or 10 day administration Device: Modified wide-angle SLO Sampling location: superior temporal quadrant Curcumin: Longvida*, 4 day administration Device: confocal SLO (Retia, CenterVue SpA) Sampling locations: 3 subregions of superotemporal quadrant posterior pole, proximal midperiphery, and distal midperiphery Curcumin: Longvida*, 4 day administration. Imaging: confocal SLO (Retia, CenterVue SpA) Sampling locations: 3 subregions of superotemporal quadrant posterior pole, proximal midperiphery, and distal midperiphery Curcumin: Longvida* 4 day administration. Imaging: confocal SLO (Retia 2 Imaging system, EyeCare) Sampling locations: central and superior retina Curcumin: Longvida* 2 day administration Imaging: SLO (Spectralis, Heidelberg Engineering) Sampling location: superolateral quadrant Curcumin: Longvida* 10 day administration, Theracurmin† (5 days), Novasol‡ (5 days) Imaging: SLO (Spectralis) Sampling locations: central macula, ONH, superior, temporal, inferior, superior temporal, and inferior temporal Ab Measurement Approach: automated fluorescent spot quantification Metric: retinal amyloid index (RAI) score Approach: automated retinal fluorescence measuring software (NeuroVision Imaging, Inc) Metrics: retinal amyloid count (RAC) and total retinal amyloid (RA) area Approach: automated retinal fluorescence measuring software (NeuroVision Imaging, Inc) Metrics: RAC and total RA area Approach: automated retinal fluorescence measuring software (NeuroVision Imaging, Inc) Metric: RAC Approach: automated retinal fluorescence measuring software; ML classifier Metrics: bilateral average of RAC from each eye and RA area; ROC analysis for discriminating AD/MCI/NC Approach: custom image processing and focal hyperfluorescent spot quantification method; qualitative visual assessment Metrics: n/a State-of-the-Art Review 171 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. TABLE 1. In vivo retinal Ab imaging studies Study Participants Imaging Methods Ab Measurement More et al (39) -15 AD (mild, moderate, and severe based on MMSE) -16 NC Approach: automated imaging analysis to calculate mean spectra for each ROI, then converted to optical density profile Metric: differential optical density (DOD) at 432 nm between AD and NCs Hadoux et al (35) -15 MCI or early AD (Ab PET+) -20 age-matched NCs (Ab PET2) Sharifi et al (40) -20 cognitively impaired (13 Ab PET+) 220 NCs (3 Ab PET+) Imaging: custom system with spatial camera, spectrographic camera (V10E imaging spectrograph, Specim, Finland), and image splitter Sampling locations: optic disc, peripapillary and central retina, and macula Imaging: metabolic Hyperspectral Retinal Camera (Optina Diagnostics, Montreal) Sampling locations: center fovea, parafovea, temporal vascular arcade superior, and inferior to fovea, temporal vascular arcade superior, and inferior to ONH Imaging: metabolic Hyperspectral Retinal Camera (Optina Diagnostics, Montreal) Sampling location: circular area centered around ONH, with adius extending 2 ONH diameters away from ONH margin Lemmens et al 2020 -10 clinically probable AD -7 biomarker positive AD-22 age-matched NCs Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 Study Curcumin Studies Koronyo et al (18) Validation -MMSE Imaging: HS snapshot camera (XIMEA SNm4x4 VIS) Sampling locations: 2 rectangular ROIs superior to the line through the center of the optic disc and fovea (near ONH and peripheral) and 2 ROIs inferior to the same line (near ONH and peripheral) Approach: linear machine learning discrimination model classification of Ab PET2 vs PET+ cases Metric: HS score obtained from model and classification accuracy Approach: 1. Sequential feature selection of top 8 HS and retinal vascular features 2. Support vector machine (SVM) classification model to discriminate between Ab PET+ and Ab PET2 cases trained on 1) vascular features alone 2)HS features alone 3) vascular and HS features combined Metric: classification accuracy Approach: nested leave-one-out crossvalidation model to classify AD and NCs trained on 1) normalized hyperspectral data and 2) combined normalized hyperspectral data and 5 RNFL features Metric: AUC Key Findings Ab Distribution -Higher RAI scores in AD vs NC (2.6-fold, P = 0.0003) -Age-matched subgroup analysis: higher RAI scores (2.1fold, P = 0.0031) in AD vs NCs -No correlation between RAI and MMSE scores -Periphery of superior quadrant -Localized to retinal blood vessels State-of-the-Art Review 172 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. (Continued ) Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 Study Validation Key Findings Ab Distribution Dumitrascu et al (27) -MRI brain volume measures: total intracranial volume, hippocampal volume, and inferior lateral ventricle volume—MoCA score -CDR score Proximal midperiphery region of the superotemporal retina was preferentially predictive of severe cognitive deficits Dumitrascu et al (28) -Retinal vascular parameters: vessel tortuosity index, vessel inflection index, and branching angle -MoCA score -CDR score -PET standardized uptake value ratio (SUVr) -Proximal midperiphery RAC and RA area were higher in subjects and lower MOCA score (P = 0.01; Cohen d = 0.83 and 0.81). -Total RAC was different between the 3 CDR groups (P = 0.023) -CDR score correlated with total RAC (r = 0.38, P = 0.02) and proximal mid-periphery amyloid count (r = 0.37, P = 0.02) -Total RAC correlated with HV (r = 20.39, P = 0.04), but on subregion analysis, only the RAC in the PMP correlated with HV -Proximal midperiphery RAC and total RAC significantly higher in the cognitively impaired vs NCs -No difference between cognitively impaired and NCs for any individual vascular parameters -Combined proximal mid-periphery RAC and venous tortuosity index variable correlated with impaired cognition. (0.49 ± 1.1 vs 0.91 ± 1.4, P = 0.006) -SUVr associated with total RAC (r: 0.65, 95% CI: 20.093, 0.930) -Ab PET- individuals had lower RAC vs NCs (difference in means: 2538, 95% CI: 2979 to 297) -RA area significantly greater in AD vs NC (P , 0.05) -RAC correlated with whole gray matter atrophy (r = 0.51, P , 0.05) -No correlation between RAC and medial temporal lobe atrophy or MMSE -AUC of 0.77 for classifying AD vs NC -Qualitative assessment: no differences in focal hyperfluorescence between groups for all 3 formulations -Quantitative assessment (Longvida* only): no difference in AD vs NC -Undetectable plasma levels of unconjugated curcumin after administration of all 3 formulations. Ngolab et al (29) Tadokoro et al (30) MRI brain volume measures: whole gray matter atrophy and medial temporal lobe atrophy -MMSE -Plasma curcumin levels assessed for 3 formulations -PET and CSF biomarker testing Proximal midperiphery region of the superotemporal retina was preferentially predictive of severe cognitive deficits RAC varied between eyes in the same individuals Perivascular area n/a State-of-the-Art Review den Haan et al (31) 173 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. (Continued ) Study Hyperspectral imaging studies More et al (39) Hadoux et al (35) Validation Key Findings Ab Distribution -Previous mouse model studies to establish HS imaging signature of soluble retinal Ab aggregates -HS score difference in 5 · FAD mice vs NCs, AUC 0.86 -Highest DOD values obtained at MCI stage, MMSE $ 22 -Highest DOD values observed over shorter wavelengths (450–510 nm) -Highest DOD in optic disc region -HS scores higher in AD vs NC for all locations -AUC of 0.87 on validation cohort (4 AD, 12 NC) -No correlations between HS score and age -Significant differences in tortuosity of retinal venules and arterioles, arterioles diameter, and arteriole HS texture measures between Ab PET+ and Ab PET2 -Model performance with HSl features alone: 74% accuracy -Best model performance with combination of hyperspectral and vascular features: 85% accuracy. -AUC of 0.74 using a combination of 14 HS features and 5 RNFL measures Most significant difference between AD vs NC in temporal vascular arcade superior to the fovea Location results available for arteriole diameter variable only: differences between Ab PET+ and Ab PET2 in 2 ROIs: Region 0–0.5 ONH diameters from ONH and region 1.5–2 ONH diameters from ONH Most informative regions: inferior and superior peripheral Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 Sharifi et al (40) Retinal vascular metrics: tortuosity, diameter measured in 3 zones, and arteriovenule ratio Lemmens et al 2020 RNFL thickness measurements on OCT (average thickness and thickness of superior, inferior, temporal, and nasal quadrants) *Verdure Sciences, Noblesville, IN. † Integrative Therapeutics LLC, Green Bay, WI. ‡ Molecular Health Technologies LLC, Millburn, NJ. AD, Alzheimer’s disease; AUC, area under the receiver operating characteristic curve; Ab, amyloid beta; CDR, Clinical Dementia Rating; CSF, cerebrospinal fluid; FTD, frontotemporal dementia; HS, hyperspectral; MCI, mild cognitive impairment; ML, machine learning; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; NC, normal control; OCT, optical coherence tomography; right eye, optical density; ONH, optic nerve head; PET, positron emission tomography; RA, retinal amyloid; RAC, retinal amyloid count; RAI, retinal amyloid index; RNFL, retinal nerve fiber layer; ROC, receiver-operator characteristics; SLO, scanning laser ophthalmoscopy; SUVr, standard uptake value ratio; SVM, support vector machine; WT, wild-type. State-of-the-Art Review 174 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. (Continued ) State-of-the-Art Review FIG. 2. A–D. Representative hyperspectral montages of 4 eyes (2 controls, 2 cases), from 450 to 900 nm in 50 nm steps, demonstrating the inherent variability within and between eyes owing to the retinal and choroidal vasculature, ocular pigment, and ocular media. E. Systematic sampling method used to analyze HS images including foveal locations (F1, F2) as well as locations superior (S1, S2) and inferior (I1, I2) to the temporal vascular arcades with segmentation of the major innerretinal blood vessels. F–K. Centered reflectance spectra at the different sampling locations for controls (n = 20, blue) and cases (n = 15, red) highlighting the large degree of intersubject variability using uncorrected spectral data, which precludes discrimination between cases and controls. Centered spectra were obtained by subtracting the average spectrum from the spectrum measured for individual participants in the principal cohort (Ab PET+ and PET2). Data shown as mean ± SEM. Adapted from Hadoux X, Hui F, Lim JKH, Masters CL, Pébay A, Chevalier S, Ha J, Loi S, Fowler CJ, Rowe C, Villemagne VL, Taylor EN, Fluke C, Soucy JP, Lesage F, Sylvestre JP, Rosa-Neto P, Mathotaarachchi S, Gauthier S, Nasreddine ZS, Arbour JD, Rhéaume MA, Beaulieu S, Dirani M, Nguyen CTO, Bui BV, Williamson R, Crowston JG, van Wijngaarden P. Non-invasive in vivo hyperspectral imaging of the retina for potential biomarker use in Alzheimer’s disease. Nat Commun. 2019;10:4227. raw reflectance data (not adjusted for inter-subject variability) between cases and controls were not significant. Other studies have combined HS imaging with other features to predict AD. Sharafi et al (40) imaged the retinas of 26 cognitively impaired and 20 NCs to derive retinal vascular metrics (tortuosity and diameter) as well as HS texture measures. A classification model was trained to predict amyloid PET status from the imaging features (40). The model achieved 76% accuracy using the vascular metrics alone and 85% accuracy when HS features were added to the model. HS features in the 450–550 nm range, consistent with the proposed HS signal of retinal Ab aggregates, differed most significantly between Ab+ and Ab2 participants. Similarly, Lemmens et al (41) developed a model to predict AD from a combination of HS imaging features and RNFL thickness measures in 17 AD participants (7 biomarker posTang et al: J Neuro-Ophthalmol 2023; 43: 168-179 itive) and 22 age-matched NC participants. The bestperforming model used a combination of 14 spectral features from a single region of interest (ROIs) and 5 RNFL measures to achieve an AUC of 0.74 (95% CI: 0.60, 0.89). The ROI superior and peripheral to the ONH demonstrated the largest differences in mean reflectance spectra between cases and controls, whereas the best performing model relied on the inferior peripheral ROI. In contrast with Hadoux et al, HS features alone were not sufficient to predict AD. Although HS imaging shows promise, further validation is needed. Confounding signals from other retinal features require a standardized approach, including specified ROIs and effective methods to correct for individual variability. HS imaging does not provide information related to Ab quantification or location and may be most effective as a biomarker of AD when combined with other retinal imaging features. 175 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. Study Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 Cases Method Stains Key Findings Schon et al (20) -Mice expressing human mutant P301S tau— human AD postmortem retinas (n = 6) In vivo mouse imaging: fluorophore FSB labeling, SLO (modified Spectralis HRA+ OCT system) FSB* AT8† AT100‡ Ho et al (21) -11 AD cases (9 neuropathology confirmed and 2 probable) -6 age‐matched NCs Immunohistochemistry analysis of paraffin‐embedded retina and brain tissue sections AT8† In vivo mouse retina: FSB positive in GCL, increasing with age, none in age-matched wt mice. No neuronal loss Ex vivo mouse retina: FSB-positive cells, also counterstaining for AT8 and AT100, preceding cortical pathology Ex vivo human retina: Intracellular AT8-positive in INL, diffuse AT8 staining in plexiform layers and GCL: negative co-staining with FSB and AT-100 -Negative AT8 staining in AD and NCs -AD brain sections positive for AT8 den Haan et al (25) -6 neuropathologyconfirmed AD -6 NCs Immunohistochemistry analysis of paraffin‐embedded retinal and hippocampal tissue sections AT8† HT7§ AT-100‡ AT-270k pS422¶ MC-1# Grimaldi et al (48) -10 neuropathologyconfirmed AD -10 age-matched NCs Immunohistochemistry analysis paraffin‐embedded retinal tissue Analysis of neurodegeneration using confocal images of cells positive for cleaved caspase 3 AT8† AT100‡ -HT7 positive in 3 distinct layers in the IPL and diffusely positive in OPL in AD vs NCs -Diffuse AT8, AT100, and AT270 axonal positivity in IPL and OPL in AD vs NCs -Overall increased staining for AT8, AT100, and AT270 in AD vs NCs -no NFTs, neuropil threads, or neuritic plaques observed in AD or NC retinas -AT8 staining most apparent in superior and peripheral regions -Two NCs showed similar AT8 staining patterns as AD cases -pS422 and MC-1 negative for AD and NCs -No Ab/APP related differences in AD retina vs NCs -Strong diffuse AT8 staining in IPL and OPL (plexiform layers) in 6 of 10 AD cases -Colabeling with AT-100 and TUJ1 localized pTau expression to inner layer RGCs -Overall higher pTau immunoreactivity in AD retina vs NCs -Neurodegeneration: number of caspase-3 positive retinal ganglion cells was increased in AD vs NCs State-of-the-Art Review 176 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. TABLE 2. Retinal tau studies *Fluorophore FSB (1-fluoro-2,5-bis(3-hydroxycarbonyl-4-hydroxy)styrylbenzene) staining for fibrillar tau aggregates. † pTau (Ser202, Thr205) monoclonal antibody. ‡ pTau (Ser 212, Thr 214) monoclonal antibody. § Pan-tau monoclonal antibody. k pTau (Thr 181) monoclonal antibody. ¶ pTau (Ser 422) monoclonal antibody. # Tau paired helical filament monoclonal antibody. ** 3R and 4R tau isoform monoclonal antibodies. AD, Alzheimer’s disease; APP, amyloid precursor protein; Ab, amyloid beta; GCL, ganglion cell layer; INL, inner nuclear layer; IPL, inner plexiform layer; NC, normal control; NFT, neurofibrillary tangle; OCT, optical coherence tomography; OPL, outer plexiform layer; pTau, hyperphosphorylated tau; RGC, retinal ganglion cell; SLO, scanning laser ophthalmoscopy. -AT8 positive in AD vs NCs -HT7 positive in both AD and controls -RD3/RD4 staining in inner and outer plexiform layers, with greater RD4 in outer plexiform layer -pS422 negative for AD and NCs HT7§ AT8† RD3/RD4** pS422¶ Immunohistochemistry analysis of paraffin‐embedded retinal and frontal cortex tissue sections Hart de Ruyter et al (Abstract) (58) Study (Continued ) -6 AD -10 NCs Cases Method Stains Key Findings State-of-the-Art Review Tang et al: J Neuro-Ophthalmol 2023; 43: 168-179 RETINAL TAU Tau is a microtubule-associated protein that is involved in the assembly and maintenance of axonal microtubules (42). Tau levels increase in AD, and hyperphosphorylation leads to abnormal intraneuronal tangles of paired helical and straight pTau filaments, leading to structural disruption and axonal transport impairment (43). In addition to filaments, pTau can also be present in the form of smaller oligomers, which may be an early manifestation of pTau pathology more closely associated with cognitive decline (44). Enzyme-linked immunosorbent assay-based CSF measures of total tau and pTau are established biomarkers of early AD but are expensive, invasive, and typically only available at large research centers (45). Retinal tau is less well-studied than Ab, but represents another potential avenue for AD biomarker research, especially as growing evidence suggests that pTau pathology may be more closely tied to cognitive decline than Ab pathology (46,47). Unlike Ab, tau has not been imaged in the human retina in vivo. One study used a modified SLO system for in vivo retinal imaging of fibrillar tau in transgenic mice expressing human P301S mutant tau (20). Mouse retinas were imaged 24 hours after the administration of fluorophore FSB (1fluoro-2,5-bis(3-hydroxycarbonyl-4-hydroxy)styrylbenzene). Fibrillar tau in the GC layer increased with age, whereas no tau was identified in age-matched wild type mice. Immunohistochemical staining of retinal and brain tissue from the same mice using AT8 and AT100 anti-pTau antibodies suggested that retinal tau preceded cortical tau pathology. Most of the evidence related to tau accumulation in the retinas of patients with AD is from histopathology studies, which have shown varied results (Table 2). Schon et al compared postmortem eyes of 6 expert-confirmed AD cases and 4 NCs stained with antibodies against hyperphosphorylated tau and fluorophore FSB. Five of the AD retinas were AT8 positive, with diffuse staining in the plexiform and GC layers but localized to individual cells in the inner nuclear layer. Costaining with fluorophore FSB was negative in the AT8-positive cells, however, as was staining for other pTau isotopes (AT100, AT180, and AT270) (20). Den Haan et al found significantly greater diffuse staining for pTau (AT8, AT100, AT270) in the inner and outer plexiform layers in AD vs NCs, although 2 NC cases showed similar patterns of AT8 staining as the AD cases. Although NFTs were observed in cortical tissue of the patients with AD, none were identified in the retina (25). Grimaldi et al (48) found pTau localized to RG cells in the inner plexiform layer and evidence of RG cell neurodegeneration in the retinas of neuropathologically confirmed patients with AD. Although the histopathological evidence seems promising, methods for imaging retinal tau remain elusive. It has not been established whether retinal tau is the same as tau pathology in the brain nor whether retinal tau is associated with clinical or neuropathological manifestations of AD. Similar to Ab pathology, tauopathy in the brain is present 177 Copyright © North American Neuro-Ophthalmology Society. Unauthorized reproduction of this article is prohibited. State-of-the-Art Review in many neurodegenerative diseases and is not specific to AD. Further complicating the picture, diffuse tau immunoreactivity in the retina is associated with aging and with other ophthalmic diseases (49). In addition to the technical challenge of imaging these proteins in the retina, establishing the validity of proteinopathies in the retina as biomarkers of AD will require longitudinal studies in wellcharacterized study populations. FUTURE DIRECTIONS Novel retinal imaging modalities may have applications for identifying Ab and tau pathology in the retina. Nearinfrared fluorescence (NIRF) imaging has been used to detect Ab in the retinas of transgenic AD mice and to differentiate AD from wild type mice (50). Other Ab labeling approaches, such as nanobodies and fluorescent probes, have been used to identify Ab in young AD mice, before cerebral Ab was detected, and in postmortem human retinas (51,52). Small molecule retinal tau tracers are in the early stages of development (53). These approaches require oral or parenteral administration of specific probes (not yet approved for use in humans) before retinal imaging, which limits their usefulness as screening devices. Fluorescence lifetime imaging ophthalmoscopy (FLIO) has been used in pilot studies to assess several retinal diseases (54,55). Sadda et al (56) found that FLIO-derived parameters in AD participants differed significantly from controls and correlated with Ab and tau CSF measures in a small crosssectional study. Although intriguing, this approach can only identify AD-related retinal abnormalities and does not directly confirm the presence of Ab and tau pathology in the retina. Artificial intelligence approaches to retinal imaging analysis are rapidly advancing (57) and are likely the key to enabling noninvasive identification of patients with retinal AD-related changes including Ab and tau pathology. ML models can incorporate data from multiple imaging modalities and/or rely on multiple features to diagnose and predict disease progression. These approaches may lead to the discovery of a combination of retinal imaging features, including those related to Ab and tau pathology, that together provide a characteristic profile of AD-related retinal pathology. CONCLUSION Despite the challenges with imaging Ab and pTau pathology in the retina, these proteins still hold promise as potential retinal biomarkers of AD. Identifying and elucidating their role in retinal pathology may provide key insights into the complex processes involved in aging and in neurodegenerative disease. Ideally, an imaging signature of Ab and pTau in the retina will be identified that is specific to AD. Whether this is specific to biomarker-positive, clinical, and/or neuropathologically confirmed AD needs to be studied. Further validation of existing imaging techniques, particularly HS imaging because of its screening potential, is needed in larger study populations. Going forward, increasingly advanced arti178 ficial intelligence-based retinal imaging analysis may be critical to enable early AD diagnosis. HS imaging, fluorescence imaging, and novel future imaging techniques will likely be useful inputs in combination with other imaging biomarkers as inputs for these AI-based approaches. REFERENCES 1. 2021 Alzheimer’s disease facts and figures. Alzheimers Dement. 2021;17:327–406. 2. Holtzman DM, Morris JC, Goate AM. 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