Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status from hyperspectral retinal images

Hyperspectral retinal imaging biomarkers of ocular and systemic diseases

Hyperspectral imaging is a frontier in the field of medical imaging technology. It enables the simultaneous collection of spectroscopic and spatial data. Structural and physiological information encoded in these data can be used to identify and localise typically elusive biomarkers. Studies of retinal hyperspectral imaging have provided novel insights into disease pathophysiology and new ways of non-invasive diagnosis and monitoring of retinal and systemic diseases. This review provides a concise overview of recent advances in retinal hyperspectral imaging.

Unlocking ocular biomarkers for early detection of Alzheimer's disease

Recent evidence suggests that ocular testing holds promise as a non-invasive and cost-effective method for the early detection of Alzheimer's disease (AD). After interviews with neurologists, optometrists, primary care physicians, and ophthalmologists, the potential for ocular biomarker testing to become a standard clinical practice in the future was assessed. Ocular tests offer a non-invasive alternative to blood-based testing, capturing a substantial niche of ≈ 4 to 8 million individuals in the United States during routine eye exams. Technical requirements for broad adoption include high accuracy comparable to blood-based tests and 510(k) clearance. Ocular biomarker technology must meet the practical requirements of optometrists and ophthalmologists, including ease of implementation, automation, and a clear path to profitability. A sufficient body of evidence to support guideline inclusion, reimbursement, and clinical actionability will facilitate the adoption. As the field evolves, advances such as earlier detection of preclinical AD may further expand the role of ocular testing. HIGHLIGHTS: Ocular biomarkers offer another non-invasive alternative to blood-based Alzheimer's disease testing. Wide adoption will require accuracy akin to blood-based tests and 510(k) clearance. Ocular screening could benefit ≈ 4 to 8 million US individuals conducting routine eye exams. Current ocular offerings remain nascent, and advances could expand this reach. New technology must show ease of implementation, automation, and a path to profit.

Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and inflammation. Additionally, next-generation PET tracers targeting misfolded proteins, such as tau and alpha-synuclein, along with inflammatory markers, enhance the visualization and quantification of pathological processes in vivo. Deep learning models, including convolutional neural networks and multimodal transformers, further improve diagnostic accuracy by integrating multimodal imaging data and predicting disease progression. Despite challenges such as technical variability, data privacy concerns, and regulatory barriers, the potential of AI-enhanced neuroimaging to revolutionize early diagnosis and personalized treatment in neurodegenerative and neuro-ophthalmic disorders is immense. This review underscores the importance of ongoing efforts to validate, standardize, and implement these technologies to maximize their clinical impact.

Amyloid beta biomarker for dementia detection by hyperspectral ophthalmoscope images

The escalating prevalence and economic burden of dementia underscore the urgency for innovative detection methods. This study investigates the potential of hyperspectral imaging (HSI) to detect dementia by analyzing retinal changes associated with amyloid beta (Aβ) formations. Leveraging a dataset of 3,256 ophthalmoscopic images from 137 participants aged 60 to 85 years, categorized into dementia and non-dementia groups via the Mini-Mental State Examination (MMSE), we extracted features from five key regions of interest (ROIs) identified for their pronounced changes in Aβ biomarkers. The analysis revealed that gender does not significantly influence dementia levels, and no substantial spectral differences were observed within the 380 nm to 600 nm wavelength range. However, significant variations in spectral reflection intensity were noted between 600 nm and 780 nm across both genders, suggesting a potential avenue for distinguishing stages of dementia. Despite the impact of diabetes on the vascular system, its stages did not significantly influence dementia development. This research highlights the utility of HSI in identifying dementia-related retinal changes and calls for further exploration into its effectiveness as a diagnostic tool, potentially offering a non-invasive method for early detection of dementia.

Hyperspectral retinal imaging in Alzheimer's disease and age-related macular degeneration: a review

While Alzheimer's disease and other neurodegenerative diseases have traditionally been viewed as brain disorders, there is growing evidence indicating their manifestation in the eyes as well. The retina, being a developmental extension of the brain, represents the only part of the central nervous system that can be noninvasively imaged at a high spatial resolution. The discovery of the specific pathological hallmarks of Alzheimer's disease in the retina of patients holds great promise for disease diagnosis and monitoring, particularly in the early stages where disease progression can potentially be slowed. Among various retinal imaging methods, hyperspectral imaging has garnered significant attention in this field. It offers a label-free approach to detect disease biomarkers, making it especially valuable for large-scale population screening efforts. In this review, we discuss recent advances in the field and outline the current bottlenecks and enabling technologies that could propel this field toward clinical translation.

Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer's disease pathology in the eye clinic

BACKGROUND

Alzheimer's disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups.

METHODS

The BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort (n = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort (n = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aβ-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments.

RESULTS

We aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aβ- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding.

CONCLUSIONS

We envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection.

TRIAL REGISTRATION

The BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23-09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC.

Alzheimer's disease pathophysiology in the Retina

The retina is an emerging CNS target for potential noninvasive diagnosis and tracking of Alzheimer's disease (AD). Studies have identified the pathological hallmarks of AD, including amyloid β-protein (Aβ) deposits and abnormal tau protein isoforms, in the retinas of AD patients and animal models. Moreover, structural and functional vascular abnormalities such as reduced blood flow, vascular Aβ deposition, and blood-retinal barrier damage, along with inflammation and neurodegeneration, have been described in retinas of patients with mild cognitive impairment and AD dementia. Histological, biochemical, and clinical studies have demonstrated that the nature and severity of AD pathologies in the retina and brain correspond. Proteomics analysis revealed a similar pattern of dysregulated proteins and biological pathways in the retina and brain of AD patients, with enhanced inflammatory and neurodegenerative processes, impaired oxidative-phosphorylation, and mitochondrial dysfunction. Notably, investigational imaging technologies can now detect AD-specific amyloid deposits, as well as vasculopathy and neurodegeneration in the retina of living AD patients, suggesting alterations at different disease stages and links to brain pathology. Current and exploratory ophthalmic imaging modalities, such as optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy, and hyperspectral imaging, may offer promise in the clinical assessment of AD. However, further research is needed to deepen our understanding of AD's impact on the retina and its progression. To advance this field, future studies require replication in larger and diverse cohorts with confirmed AD biomarkers and standardized retinal imaging techniques. This will validate potential retinal biomarkers for AD, aiding in early screening and monitoring.

Retinal peri-arteriolar versus peri-venular amyloidosis, hippocampal atrophy, and cognitive impairment: exploratory trial

The relationship between amyloidosis and vasculature in cognitive impairment and Alzheimer's disease (AD) pathogenesis is increasingly acknowledged. We conducted a quantitative and topographic assessment of retinal perivascular amyloid plaque (AP) distribution in individuals with both normal and impaired cognition. Using a retrospective dataset of scanning laser ophthalmoscopy fluorescence images from twenty-eight subjects with varying cognitive states, we developed a novel image processing method to examine retinal peri-arteriolar and peri-venular curcumin-positive AP burden. We further correlated retinal perivascular amyloidosis with neuroimaging measures and neurocognitive scores. Our study unveiled that peri-arteriolar AP counts surpassed peri-venular counts throughout the entire cohort (P < 0.0001), irrespective of the primary, secondary, or tertiary vascular branch location, with a notable increase among cognitively impaired individuals. Moreover, secondary branch peri-venular AP count was elevated in the cognitively impaired (P < 0.01). Significantly, peri-venular AP count, particularly in secondary and tertiary venules, exhibited a strong correlation with clinical dementia rating, Montreal cognitive assessment score, hippocampal volume, and white matter hyperintensity count. In conclusion, our exploratory analysis detected greater peri-arteriolar versus peri-venular amyloidosis and a marked elevation of amyloid deposition in secondary branch peri-venular regions among cognitively impaired subjects. These findings underscore the potential feasibility of retinal perivascular amyloid imaging in predicting cognitive decline and AD progression. Larger longitudinal studies encompassing diverse populations and AD-biomarker confirmation are warranted to delineate the temporal-spatial dynamics of retinal perivascular amyloid deposition in cognitive impairment and the AD continuum.

The Crosstalk Between Amyloid-β, Retina, and Sleep for the Early Diagnosis of Alzheimer's Disease: A Narrative Review

Alzheimer's disease (AD) is the most common type of dementia, which is characterised by progressive memory loss and accumulation of hallmark markers amyloid-β (Aβ) and neurofibrillary tangles in the diseased brain. The current gold standard diagnostic methods have limitations of being invasive, costly, and not easily accessible. Thus, there is a need for new avenues, such as imaging the retina for early AD diagnosis. Sleep disruption is symptomatically frequent across preclinical and AD subjects. As circadian activity, such as the sleep-wake cycle, is linked to the retina, analysis of their association may be useful additions for achieving predictive AD diagnosis. In this narrative review, we provide an overview of human retina studies concerning the deposition of Aβ, the role of the retina in sleep-wake cycle, the disruption of sleep in AD, and to gather evidence for the associations between Aβ, the retina, and sleep. Understanding the mechanisms behind the associations between Aβ, retina, and sleep could assist in the interpretation of retinal changes accurately in AD.

Glaucoma Detection through a Novel Hyperspectral Imaging Band Selection and Vision Transformer Integration

Conventional diagnostic methods for glaucoma primarily rely on non-dynamic fundus images and often analyze features such as the optic cup-to-disc ratio and abnormalities in specific retinal locations like the macula and fovea. However, hyperspectral imaging techniques focus on detecting alterations in oxygen saturation within retinal vessels, offering a potentially more comprehensive approach to diagnosis. This study explores the diagnostic potential of hyperspectral imaging for glaucoma by introducing a novel hyperspectral imaging conversion technique. Digital fundus images are transformed into hyperspectral representations, allowing for a detailed analysis of spectral variations. Spectral regions exhibiting differences are identified through spectral analysis, and images are reconstructed from these specific regions. The Vision Transformer (ViT) algorithm is then employed for classification and comparison across selected spectral bands. Fundus images are used to identify differences in lesions, utilizing a dataset of 1291 images. This study evaluates the classification performance of models using various spectral bands, revealing that the 610-780 nm band outperforms others with an accuracy, precision, recall, F1-score, and AUC-ROC all approximately at 0.9007, indicating its superior effectiveness for the task. The RGB model also shows strong performance, while other bands exhibit lower recall and overall metrics. This research highlights the disparities between machine learning algorithms and traditional clinical approaches in fundus image analysis. The findings suggest that hyperspectral imaging, coupled with advanced computational techniques such as the ViT algorithm, could significantly enhance glaucoma diagnosis. This understanding offers insights into the potential transformation of glaucoma diagnostics through the integration of hyperspectral imaging and innovative computational methodologies.

Distinct retinal reflectance spectra from retinal hyperspectral imaging in Parkinson's disease

BACKGROUND

Recent developments in the retinal hyperspectral imaging method have indicated its potential in addressing challenges posed by neurodegenerative disorders, such as Alzheimer's disease. This human clinical study is the first to assess reflectance spectra obtained from this imaging as a tool for diagnosing patients with Parkinson's disease (PD).

METHODS

Retinal hyperspectral imaging was conducted on a total of 40 participants, including 20 patients with PD and 20 controls. Following preprocessing, retinal reflectance spectra were computed for the macular retina defined by four rectangular regions. Linear discriminant analysis classifiers underwent training to discern patients with PD from control participants. To assess the performance of the selected features, nested leave-one-out cross-validation was employed using machine learning. The indicated values include the area under the curve (AUC) and the corresponding 95% confidence interval (CI).

RESULTS

Retinal reflectance spectra of PD patients exhibited variations in the spectral regions, particularly at shorter wavelengths (superonasal retina, wavelength < 490 nm; inferonasal retina, wavelength < 510 nm) when compared to those of controls. Retinal reflectance spectra yielded an AUC of 0.60 (95% CI: 0.43-0.78) and 0.60 (95% CI: 0.43-0.78) for the superonasal and inferonasal retina, respectively, distinguishing individuals with and without PD.

CONCLUSION

Reflectance spectra obtained from retinal hyperspectral imaging tended to decrease at shorter wavelengths across a broad spectral range in PD patients. Further investigations building upon these preliminary findings are imperative to focus on the retinal spectral signatures associated with PD pathological hallmarks, including α-synuclein.

Naevi Characterization Using Hyperspectral Imaging: A Pilot Study

PURPOSE

The prevalence of choroidal naevi is common and has been found to be up to 10%. Little is known regarding the optical properties of choroidal naevi. A novel hyperspectral eye fundus camera was used to investigate choroidal naevi's optical density spectra in the retina.

METHODS

In an ophthalmology clinic setting, patients with choroidal naevi were included in the study. Visual acuity and pressure were tested. Following mydriatics, optical coherence tomography and fundus photography were taken as a reference, after which a hyperspectral image with 12 nm spectral resolution at 450-700 nm was taken. The optical density spectra was measured across the area of the naevus.

RESULTS

Nine patients with 11 naevi were examined. The visual acuity was not affected by any of the naevi. All the naevi were flat as measured either with the optical coherence tomography and/or on inspection, and only one naevi had a risk factor (orange pigmentation). The Wasserstein distance between the background and the naevi was higher at 695 nm compared to 555 nm (p = .002). The naevi could be grouped into three clusters based on the extracted optical density spectra.

CONCLUSION

Choroidal naevi are better visible in longer wavelengths compared to shorter wavelengths. This finding can be used to contour and follow choroidal naevi. Choroidal naevi expose different optical density spectra that can be grouped into three different clusters. One of these clusters has an optical density spectra resembling the absorption spectra of lipofuscin, which may indicate the content of this pigment.

The AUstralian multidomain Approach to Reduce dementia Risk by prOtecting brain health With lifestyle intervention study (AU-ARROW): A study protocol for a single-blind, multi-site, randomized controlled trial

INTRODUCTION

The Finnish Geriatric Intervention Study (FINGER) led to the global dementia risk reduction initiative: World-Wide FINGERS (WW-FINGERS). As part of WW-FINGERS, the Australian AU-ARROW study mirrors aspects of FINGER, as well as US-POINTER.

METHOD

AU-ARROW is a randomized, single-blind, multisite, 2-year clinical trial (n = 600; aged 55-79). The multimodal lifestyle intervention group will engage in aerobic exercise, resistance training and stretching, dietary advice to encourage MIND diet adherence, BrainHQ cognitive training, and medical monitoring and health education. The Health Education and Coaching group will receive occasional health education sessions. The primary outcome measure is the change in a global composite cognitive score. Extra value will emanate from blood biomarker analysis, positron emission tomography (PET) imaging, brain magnetic resonance imaging (MRI), and retinal biomarker tests.

DISCUSSION

The finalized AU-ARROW protocol is expected to allow development of an evidence-based innovative treatment plan to reduce cognitive decline and dementia risk, and effective transfer of research outcomes into Australian health policy.

Highlights

Study protocol for a single-blind, randomized controlled trial, the AU-ARROW Study.The AU-ARROW Study is a member of the World-Wide FINGERS (WW-FINGERS) initiative.AU-ARROW's primary outcome measure is change in a global composite cognitive score.Extra significance from amyloid PET imaging, brain MRI, and retinal biomarker tests.Leading to development of an innovative treatment plan to reduce cognitive decline.

Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics

The condition known as Plus disease is distinguished by atypical alterations in the retinal vasculature of neonates born prematurely. It has been demonstrated that the diagnosis of Plus disease is subjective and qualitative in nature. The utilization of quantitative methods and computer-based image analysis to enhance the objectivity of Plus disease diagnosis has been extensively established in the literature. This study presents the development of a computer-based image analysis method aimed at automatically distinguishing Plus images from non-Plus images. The proposed methodology conducts a quantitative analysis of the vascular characteristics linked to Plus disease, thereby aiding physicians in making informed judgments. A collection of 76 posterior retinal images from a diverse group of infants who underwent screening for Retinopathy of Prematurity (ROP) was obtained. A reference standard diagnosis was established as the majority of the labeling performed by three experts in ROP during two separate sessions. The process of segmenting retinal vessels was carried out using a semi-automatic methodology. Computer algorithms were developed to compute the tortuosity, dilation, and density of vessels in various retinal regions as potential discriminative characteristics. A classifier was provided with a set of selected features in order to distinguish between Plus images and non-Plus images. This study included 76 infants (49 [64.5%] boys) with mean birth weight of 1305 ± 427 g and mean gestational age of 29.3 ± 3 weeks. The average level of agreement among experts for the diagnosis of plus disease was found to be 79% with a standard deviation of 5.3%. In terms of intra-expert agreement, the average was 85% with a standard deviation of 3%. Furthermore, the average tortuosity of the five most tortuous vessels was significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The curvature values based on points were found to be significantly higher in Plus images compared to non-Plus images (p ≤ 0.0001). The maximum diameter of vessels within a region extending 5-disc diameters away from the border of the optic disc (referred to as 5DD) exhibited a statistically significant increase in Plus images compared to non-Plus images (p ≤ 0.0001). The density of vessels in Plus images was found to be significantly higher compared to non-Plus images (p ≤ 0.0001). The classifier's accuracy in distinguishing between Plus and non-Plus images, as determined through tenfold cross-validation, was found to be 0.86 ± 0.01. This accuracy was observed to be higher than the diagnostic accuracy of one out of three experts when compared to the reference standard. The implemented algorithm in the current study demonstrated a commendable level of accuracy in detecting Plus disease in cases of retinopathy of prematurity, exhibiting comparable performance to that of expert diagnoses. By engaging in an objective analysis of the characteristics of vessels, there exists the possibility of conducting a quantitative assessment of the disease progression's features. The utilization of this automated system has the potential to enhance physicians' ability to diagnose Plus disease, thereby offering valuable contributions to the management of ROP through the integration of traditional ophthalmoscopy and image-based telemedicine methodologies.

Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity

Retinal vascular tortuosity is an excessive bending and twisting of the blood vessels in the retina that is associated with numerous health conditions. We propose a novel methodology for the automated assessment of the retinal vascular tortuosity from color fundus images. Our methodology takes into consideration several anatomical factors to weigh the importance of each individual blood vessel. First, we use deep neural networks to produce a robust extraction of the different anatomical structures. Then, the weighting coefficients that are required for the integration of the different anatomical factors are adjusted using evolutionary computation. Finally, the proposed methodology also provides visual representations that explain the contribution of each individual blood vessel to the predicted tortuosity, hence allowing us to understand the decisions of the model. We validate our proposal in a dataset of color fundus images providing a consensus ground truth as well as the annotations of five clinical experts. Our proposal outperforms previous automated methods and offers a performance that is comparable to that of the clinical experts. Therefore, our methodology demonstrates to be a viable alternative for the assessment of the retinal vascular tortuosity. This could facilitate the use of this biomarker in clinical practice and medical research.

Distinctive retinal peri-arteriolar versus peri-venular amyloid plaque distribution correlates with the cognitive performance

Introduction

The vascular contribution to Alzheimer's disease (AD) is tightly connected to cognitive performance across the AD continuum. We topographically describe retinal perivascular amyloid plaque (AP) burden in subjects with normal or impaired cognition.

Methods

Using scanning laser ophthalmoscopy, we quantified retinal peri-arteriolar and peri-venular curcumin-positive APs in the first, secondary and tertiary branches in twenty-eight subjects. Perivascular AP burden among cognitive states was correlated with neuroimaging and cognitive measures.

Results

Peri-arteriolar exceeded peri-venular AP count (p<0.0001). Secondary branch AP count was significantly higher in cognitively impaired (p<0.01). Secondary small and tertiary peri-venular AP count strongly correlated with clinical dementia rating, hippocampal volumes, and white matter hyperintensity count.

Discussion

Our topographic analysis indicates greater retinal amyloid accumulation in the retinal peri-arteriolar regions overall, and distal peri-venular regions in cognitively impaired individuals. Larger longitudinal studies are warranted to understand the temporal-spatial relationship between vascular dysfunction and perivascular amyloid deposition in AD.

Highlights

Retinal peri-arteriolar region exhibits more amyloid compared with peri-venular regions.Secondary retinal vascular branches have significantly higher perivascular amyloid burden in subjects with impaired cognition, consistent across sexes.Cognitively impaired individuals have significantly greater retinal peri-venular amyloid deposits in the distal small branches, that correlate with CDR and hippocampal volumes.

Clearance of interstitial fluid (ISF) and CSF (CLIC) group-part of Vascular Professional Interest Area (PIA), updates in 2022-2023. Cerebrovascular disease and the failure of elimination of Amyloid-β from the brain and retina with age and Alzheimer's disease: Opportunities for therapy

This editorial summarizes advances from the Clearance of Interstitial Fluid and Cerebrospinal Fluid (CLIC) group, within the Vascular Professional Interest Area (PIA) of the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART). The overarching objectives of the CLIC group are to: (1) understand the age-related physiology changes that underlie impaired clearance of interstitial fluid (ISF) and cerebrospinal fluid (CSF) (CLIC); (2) understand the cellular and molecular mechanisms underlying intramural periarterial drainage (IPAD) in the brain; (3) establish novel diagnostic tests for Alzheimer's disease (AD), cerebral amyloid angiopathy (CAA), retinal amyloid vasculopathy, amyloid-related imaging abnormalities (ARIA) of spontaneous and iatrogenic CAA-related inflammation (CAA-ri), and vasomotion; and (4) establish novel therapies that facilitate IPAD to eliminate amyloid β (Aβ) from the aging brain and retina, to prevent or reduce AD and CAA pathology and ARIA side events associated with AD immunotherapy.

Hyperspectral Retinal Imaging as a Non-Invasive Marker to Determine Brain Amyloid Status

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.

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

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 (Aβ), 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 Aβ, although both require validation in larger cohorts. Challenges remain in distinguishing curcumin-labeled Aβ 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 Aβ 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 Aβ 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 Aβ and pTau in the retina will provide key insights into the complex processes involved in aging and in neurodegenerative disease.

Retinal imaging biomarkers of Alzheimer's disease: A systematic review and meta-analysis of studies using brain amyloid beta status for case definition

Introduction

We performed a systematic review and meta-analysis of the association between retinal imaging parameters and Alzheimer's disease (AD).

Methods

PubMed, EMBASE, and Scopus were systematically searched for prospective and observational studies. Included studies had AD case definition based on brain amyloid beta (Aβ) status. Study quality assessment was performed. Random-effects meta-analyses of standardized mean difference, correlation, and diagnostic accuracy were conducted.

Results

Thirty-eight studies were included. There was weak evidence of peripapillary retinal nerve fiber layer thinning on optical coherence tomography (OCT) (p = 0.14, 11 studies, n = 828), increased foveal avascular zone area on OCT-angiography (p = 0.18, four studies, n = 207), and reduced arteriole and venule vessel fractal dimension on fundus photography (p < 0.001 and p = 0.08, respectively, three studies, n = 297) among AD cases.

Discussion

Retinal imaging parameters appear to be associated with AD. Small study sizes and heterogeneity in imaging methods and reporting make it difficult to determine utility of these changes as AD biomarkers.

Highlights

We performed a systematic review on retinal imaging and Alzheimer's disease (AD).We only included studies in which cases were based on brain amyloid beta status.Several retinal biomarkers were associated with AD but clinical utility is uncertain.Studies should focus on biomarker-defined AD and use standardized imaging methods.

Classification of Transgenic Mice by Retinal Imaging Using SVMS

Alzheimer's disease is the neuro disorder which characterized by means of Amyloid– β (A  β) in brain. However, accurate detection of this disease is a challenging task since the pathological issues of brain are complex in identification. In this paper, the changes associated with the retinal imaging for Alzheimer's disease are classified into two classes such as wild-type (WT) and transgenic mice model (TMM). For testing, optical coherence tomography (OCT) images are used to classify into two groups. The classification is implemented by support vector machines with the optimum kernel selection using a genetic algorithm. Among several kernel functions of SVM, the radial basis kernel function provides the better classification result. In order to deal with an effective classification using SVM, texture features of retinal images are extracted and selected. The overall accuracy reached 92% and 91% of precision for the classification of transgenic mice.

Alterations in Retinal Signaling Across Age and Sex in 3xTg Alzheimer’s Disease Mice

Background:

Visual disturbances often precede cognitive dysfunction in patients with Alzheimer’s disease (AD) and may coincide with early accumulation of amyloid-β (Aβ) protein in the retina. These findings have inspired critical research on in vivo ophthalmic Aβ imaging for disease biomarker detection but have not fully answered mechanistic questions on how retinal pathology affects visual signaling between the eye and brain.

Objective:

The goal of this study was to provide a functional and structural assessment of eye-brain communication between retinal ganglion cells (RGCs) and their primary projection target, the superior colliculus, in female and male 3xTg-AD mice across disease stages.

Methods:

Retinal electrophysiology, axonal transport, and immunofluorescence were used to determine RGC projection integrity, and retinal and collicular Aβ levels were assessed with advanced protein quantitation techniques.

Results:

3xTg mice exhibited nuanced deficits in RGC electrical signaling, axonal transport, and synaptic integrity that exceeded normal age-related decrements in RGC function in age- and sex-matched healthy control mice. These deficits presented in sex-specific patterns among 3xTg mice, differing in the timing and severity of changes.

Conclusion:

These data support the premise that retinal Aβ is not just a benign biomarker in the eye, but may contribute to subtle, nuanced visual processing deficits. Such disruptions might enhance the biomarker potential of ocular amyloid and differentiate patients with incipient AD from patients experiencing normal age-related decrements in visual function.

Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database

Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheimer’s disease. Knowing from previous studies that these two factors can manifest in the retina, the aim was to investigate whether a deep learning method was able to predict the cognition of an individual from a RGB image of his retina and metadata. A deep learning model, EfficientNet, was used to predict cognitive scores from the Canadian Longitudinal Study on Aging (CLSA) database. The proposed model explained 22.4% of the variance in cognitive scores on the test dataset using fundus images and metadata. Metadata alone proved to be more effective in explaining the variance in the sample (20.4%) versus fundus images (9.3%) alone. Attention maps highlighted the optic nerve head as the most influential feature in predicting cognitive scores. The results demonstrate that RGB fundus images are limited in predicting cognition.

Retinal Venular Tortuosity Jointly with Retinal Amyloid Burden Correlates with Verbal Memory Loss: A Pilot Study

Introduction: Retinal imaging is a non-invasive tool to study both retinal vasculature and neurodegeneration. In this exploratory retinal curcumin-fluorescence imaging (RFI) study, we sought to determine whether retinal vascular features combined with retinal amyloid burden correlate with the neurocognitive status. Methods: We used quantitative RFI in a cohort of patients with cognitive impairment to automatically compute retinal amyloid burden. Retinal blood vessels were segmented, and the vessel tortuosity index (VTI), inflection index, and branching angle were quantified. We assessed the correlations between retinal vascular and amyloid parameters, and cognitive domain Z-scores using linear regression models. Results: Thirty-four subjects were enrolled and twenty-nine (55% female, mean age 64 ± 6 years) were included in the combined retinal amyloid and vascular analysis. Eleven subjects had normal cognition and 18 had impaired cognition. Retinal VTI was discriminated among cognitive scores. The combined proximal mid-periphery amyloid count and venous VTI index exhibited significant differences between cognitively impaired and cognitively normal subjects (0.49 ± 1.1 vs. 0.91 ± 1.4, p = 0.006), and correlated with both the Wechsler Memory Scale-IV and SF-36 mental component score Z-scores (p < 0.05). Conclusion: This pilot study showed that retinal venular VTI combined with the proximal mid-periphery amyloid count could predict verbal memory loss. Future research is needed to finesse the clinical application of this retinal imaging-based technology.

Changes in retinal multilayer thickness and vascular network of patients with Alzheimer's disease

BACKGROUND

Retinal biomarkers of Alzheimer's disease (AD) have been extensively investigated in recent decades. Retinal nervous and vascular parameters can reflect brain conditions, and they can facilitate early diagnosis of AD.

OBJECTIVE

Our study aimed to evaluate the difference in retinal neuro-layer thickness and vascular parameters of patients with AD and healthy controls (HCs).

METHODS

Non-invasive optical coherence tomography angiography (OCTA) was used to determine the combined thickness of the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL), as well as the full retinal thickness (FRT). The vascular branching (VB), vascular curvature (VC), and vascular density (VD) for AD and HC groups were also obtained. The Mini-Mental State Examination (MMSE) was used to evaluate the cognitive performance of all the participants. After obtaining all the parameters, two-way analysis of variance (ANOVA) was used to compare the mean values of all the retinal parameters of the patients with AD and the HCs. Pearson's correlation was used to test the association between retinal parameters, MMSE scores, and vascular parameters.

RESULTS

Seventy-eight eyes from 39 participants (19 AD and 20 HC; male, 52.6% in AD and 45.0% in HC; mean [standard deviation] age of 73.79 [7.22] years in AD and 74.35 [6.07] years in HC) were included for the analysis. The average RNFL + GCL thickness (106.32 ± 7.34 μm), FRTs of the four quadrants (290.35 ± 13.05 μm of inferior quadrant, 294.68 ± 9.37 μm of superior quadrant, 302.97 ± 6.52 μm of nasal quadrant, 286.02 ± 13.74 μm of temporal quadrant), and retinal VD (0.0148 ± 0.003) of patients with AD, compared with the HCs, were significantly reduced (p < 0.05). Retinal thickness was significantly correlated with the MMSE scores (p < 0.05). Meanwhile, retinal VD was significantly correlated with the average RNFL + GCL thickness (r2 = 0.2146, p < 0.01). When the vascular parameters were considered, the sensitivity of the AD diagnosis was increased from 0.874 to 0.892.

CONCLUSION

Our study suggested that the patients with AD, compared with age-matched HCs, had significantly reduced RNFL + GCL thickness and vascular density. These reductions correlated with the cognitive performance of the participants. By combining nerve and vessel parameters, the diagnosis of AD can be improved using OCTA technology. Trail registration Name of the registry: Chinese Clinical Trail Registry, Trial registration number: ChiCTR2000035243, Date of registration: Aug. 5, 2020. URL of trial registry record: http://www.chictr.org.cn/index.aspx.

Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study

Purpose

Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left).

Methods

A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2).

Results

For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively.

Conclusions

Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities.

Translational Relevance

The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.

Retinal Dysfunction in Alzheimer's Disease and Implications for Biomarkers

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that manifests as cognitive deficits and memory decline, especially in old age. Several biomarkers have been developed to monitor AD progression. Given that the retina and brain share some similarities including features related to anatomical composition and neurological functions, the retina is closely associated with the progression of AD. Herein, we review the evidence of retinal dysfunction in AD, particularly at the early stage, together with the underlying molecular mechanisms. Furthermore, we compared the retinal pathologies of AD and other ophthalmological diseases and summarized potential retinal biomarkers measurable by existing technologies for detecting AD, providing insights for the future development of diagnostic tools.

Retinal Amyloid Imaging for Screening Alzheimer's Disease

BACKGROUND

Cost-effective and noninvasive methods for in vivo imaging of amyloid deposition are needed to screen Alzheimer's disease (AD). Although retinal amyloid is a possible diagnostic marker of AD, there are very few studies on in vivo retinal amyloid imaging.

OBJECTIVE

To examine the usefulness of in vivo imaging of retinal amyloid in AD patients.

METHODS

To examine amyloid deposition, 30 Japanese subjects (10 normal control (NC), 7 with mild cognitive impairment (MCI), and 13 with AD) underwent a complete ophthalmic examination, including fundus imaging by scanning laser ophthalmoscopy before and after oral curcumin intake.

RESULTS

Retinal amyloid deposition was greater in AD than in NC subjects (*p < 0.05) while MCI showed a slight but insignificant increase of retinal amyloid deposition relative to NC subjects. Retinal amyloid deposition was correlated with whole gray matter atrophy (r = 0.51, *p < 0.05) but not with the cognitive score of the Mini-Mental State Examination, nor with medial temporal lobe atrophy.

CONCLUSION

The present noninvasive in vivo detection of retinal amyloid deposition is useful for screening AD patients.

Past, present and future role of retinal imaging in neurodegenerative disease

Retinal imaging technology is rapidly advancing and can provide ever-increasing amounts of information about the structure, function and molecular composition of retinal tissue in humans in vivo. Most importantly, this information can be obtained rapidly, non-invasively and in many cases using Food and Drug Administration-approved devices that are commercially available. Technologies such as optical coherence tomography have dramatically changed our understanding of retinal disease and in many cases have significantly improved their clinical management. Since the retina is an extension of the brain and shares a common embryological origin with the central nervous system, there has also been intense interest in leveraging the expanding armamentarium of retinal imaging technology to understand, diagnose and monitor neurological diseases. This is particularly appealing because of the high spatial resolution, relatively low-cost and wide availability of retinal imaging modalities such as fundus photography or OCT compared to brain imaging modalities such as magnetic resonance imaging or positron emission tomography. The purpose of this article is to review and synthesize current research about retinal imaging in neurodegenerative disease by providing examples from the literature and elaborating on limitations, challenges and future directions. We begin by providing a general background of the most relevant retinal imaging modalities to ensure that the reader has a foundation on which to understand the clinical studies that are subsequently discussed. We then review the application and results of retinal imaging methodologies to several prevalent neurodegenerative diseases where extensive work has been done including sporadic late onset Alzheimer's Disease, Parkinson's Disease and Huntington's Disease. We also discuss Autosomal Dominant Alzheimer's Disease and cerebrovascular small vessel disease, where the application of retinal imaging holds promise but data is currently scarce. Although cerebrovascular disease is not generally considered a neurodegenerative process, it is both a confounder and contributor to neurodegenerative disease processes that requires more attention. Finally, we discuss ongoing efforts to overcome the limitations in the field and unmet clinical and scientific needs.

Heart, Eye, and Artificial Intelligence: A Review

Heart disease continues to be the leading cause of death in the USA. Deep learning-based artificial intelligence (AI) methods have become increasingly common in studying the various factors involved in cardiovascular disease. The usage of retinal scanning techniques to diagnose retinal diseases, such as diabetic retinopathy, age-related macular degeneration, glaucoma and others, using fundus photographs and optical coherence tomography angiography (OCTA) has been extensively documented. Researchers are now looking to combine the power of AI with the non-invasive ease of retinal scanning to examine the workings of the heart and predict changes in the macrovasculature based on microvascular features and function. In this review, we summarize the current state of the field in using retinal imaging to diagnose cardiovascular issues and other diseases.

Role of Retinal Amyloid-β in Neurodegenerative Diseases: Overlapping Mechanisms and Emerging Clinical Applications

Amyloid-β (Aβ) accumulations have been identified in the retina for neurodegeneration-associated disorders like Alzheimer's disease (AD), glaucoma, and age-related macular degeneration (AMD). Elevated retinal Aβ levels were associated with progressive retinal neurodegeneration, elevated cerebral Aβ accumulation, and increased disease severity with a decline in cognition and vision. Retinal Aβ accumulation and its pathological effects were demonstrated to occur prior to irreversible neurodegeneration, which highlights its potential in early disease detection and intervention. Using the retina as a model of the brain, recent studies have focused on characterizing retinal Aβ to determine its applicability for population-based screening of AD, which warrants a further understanding of how Aβ manifests between these disorders. While current treatments directly targeting Aβ accumulations have had limited results, continued exploration of Aβ-associated pathological pathways may yield new therapeutic targets for preserving cognition and vision. Here, we provide a review on the role of retinal Aβ manifestations in these distinct neurodegeneration-associated disorders. We also discuss the recent applications of retinal Aβ for AD screening and current clinical trial outcomes for Aβ-associated treatment approaches. Lastly, we explore potential future therapeutic targets based on overlapping mechanisms of pathophysiology in AD, glaucoma, and AMD.

Acute effects of high-intensity interval training on cognition and retinal microcirculation in experienced amateur boxers

BACKGROUND

Limited and contradictory data are available regarding the acute effects of endurance exercises on cognition and retinal microcirculation. Thus, we aimed to evaluate the acute effects of sport-specific high-intensity interval training (HIIT) on cognition and retinal vessel diameters in experienced amateur boxers.

METHODS

Eleven young (age 22.8±2.9 years) men with 10.7±4.8 years of boxing experience completed two sessions in random order: high-intensity intermittent punching load and passive rest. Cardiovascular response, retinal vessel diameters, and cognitive performance were evaluated at baseline, during exercise, and immediately after each session.

RESULTS

Increased heart rate during intermittent punching load (P<0.05) reaching 91.2±3.7% of the maximal heart rate was observed. The HIIT improved cognitive flexibility and inhibitory control (P<0.05), while the working memory and motor speed were not affected. Significant dilatation of temporal retinal venules (P<0.05) was observed after the HIIT compared with the values before the HIIT, resulting in a decreased arteriolar-to-venular diameter ratio (P<0.05).

CONCLUSIONS

At the functional level, an improvement in executive function due to intermittent high intensity punching load was observed, while at the physiological level, retinal venular dilatation was observed in experienced amateur boxers.

Combination of snapshot hyperspectral retinal imaging and optical coherence tomography to identify Alzheimer's disease patients

INTRODUCTION

The eye offers potential for the diagnosis of Alzheimer's disease (AD) with retinal imaging techniques being explored to quantify amyloid accumulation and aspects of neurodegeneration. To assess these changes, this proof-of-concept study combined hyperspectral imaging and optical coherence tomography to build a classification model to differentiate between AD patients and controls.

METHODS

In a memory clinic setting, patients with a diagnosis of clinically probable AD (n = 10) or biomarker-proven AD (n = 7) and controls (n = 22) underwent non-invasive retinal imaging with an easy-to-use hyperspectral snapshot camera that collects information from 16 spectral bands (460-620 nm, 10-nm bandwidth) in one capture. The individuals were also imaged using optical coherence tomography for assessing retinal nerve fiber layer thickness (RNFL). Dedicated image preprocessing analysis was followed by machine learning to discriminate between both groups.

RESULTS

Hyperspectral data and retinal nerve fiber layer thickness data were used in a linear discriminant classification model to discriminate between AD patients and controls. Nested leave-one-out cross-validation resulted in a fair accuracy, providing an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [0.60-0.89]). Inner loop results showed that the inclusion of the RNFL features resulted in an improvement of the area under the receiver operating characteristic curve: for the most informative region assessed, the average area under the receiver operating characteristic curve was 0.70 (95% confidence interval [0.55, 0.86]) and 0.79 (95% confidence interval [0.65, 0.93]), respectively. The robust statistics used in this study reduces the risk of overfitting and partly compensates for the limited sample size.

CONCLUSIONS

This study in a memory-clinic-based cohort supports the potential of hyperspectral imaging and suggests an added value of combining retinal imaging modalities. Standardization and longitudinal data on fully amyloid-phenotyped cohorts are required to elucidate the relationship between retinal structure and cognitive function and to evaluate the robustness of the classification model.

A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre

Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs.

Sectoral segmentation of retinal amyloid imaging in subjects with cognitive decline

INTRODUCTION

Despite advances in imaging retinal amyloidosis, a quantitative and topographical investigation of retinal amyloid beta burden in patients with cognitive decline has never been reported.

METHODS

We used the specific amyloid-binding fluorophore curcumin and laser ophthalmoscopy to assess retinal amyloid imaging (RAI) in 34 patients with cognitive decline. We automatically quantified retinal amyloid count (RAC) and area in the superotemporal retinal sub-regions and performed correlation analyses with cognitive and brain volumetric parameters.

RESULTS

RAC significantly and inversely correlated with hippocampal volume (HV; r = -0.39, P = .04). The proximal mid-periphery (PMP) RAC and RA areas were significantly greater in patients with Montreal Cognitive Assessment (MOCA) score < 26 (P = .01; Cohen d = 0.83 and 0.81, respectively). PMP showed significantly more RAC and area in subjects with amnestic mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared to cognitively normal (P = .04; Cohen d = 0.83).

CONCLUSION

Quantitative RAI is a feasible technique and PMP RAC may predict HV. Future larger studies should determine RAI's potential as a biomarker of early AD.

Environment-Sensitive Probes for Illuminating Amyloid Aggregation In Vitro and in Zebrafish

The aberrant aggregation of certain peptides and proteins, forming extracellular plaques of fibrillar material, is one of the hallmarks of amyloid diseases, such as Alzheimer's and Parkinson's. Herein, we have designed a new family of solvatochromic dyes based on the 9-amino-quinolimide moiety capable of reporting during the early stages of amyloid fibrillization. We have rationally improved the photophysical properties of quinolimides by placing diverse amino groups at the 9-position of the quinolimide core, leading to higher solvatochromic and fluorogenic character and higher lifetime dependence on the hydrophobicity of the environment, which represent excellent properties for the sensitive detection of prefibrillar aggregates. Among the different probes prepared, the 9-azetidinyl-quinolimide derivative showed striking performance in the following β-amyloid peptide (Aβ) aggregation in solution in real time and identifying the formation of different types of early oligomers of Aβ, the most important species linked to cytotoxicity, using novel, multidimensional fluorescence microscopy, with one- or two-photon excitation. Interestingly, the new dye allowed the visualization of proteinaceous inclusion bodies in a zebrafish model with neuronal damage induced by the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Our results support the potential of the novel fluorophores as powerful tools to follow amyloid aggregation using fluorescence microscopy in vivo, revealing heterogeneous populations of different types of aggregates and, more broadly, to study protein interactions.

Alzheimer's Retinopathy: Seeing Disease in the Eyes

The neurosensory retina emerges as a prominent site of Alzheimer's disease (AD) pathology. As a CNS extension of the brain, the neuro retina is easily accessible for noninvasive, high-resolution imaging. Studies have shown that along with cognitive decline, patients with mild cognitive impairment (MCI) and AD often suffer from visual impairments, abnormal electroretinogram patterns, and circadian rhythm disturbances that can, at least in part, be attributed to retinal damage. Over a decade ago, our group identified the main pathological hallmark of AD, amyloid β-protein (Aβ) plaques, in the retina of patients including early-stage clinical cases. Subsequent histological, biochemical and in vivo retinal imaging studies in animal models and in humans corroborated these findings and further revealed other signs of AD neuropathology in the retina. Among these signs, hyperphosphorylated tau, neuronal degeneration, retinal thinning, vascular abnormalities and gliosis were documented. Further, linear correlations between the severity of retinal and brain Aβ concentrations and plaque pathology were described. More recently, extensive retinal pericyte loss along with vascular platelet-derived growth factor receptor-β deficiency were discovered in postmortem retinas of MCI and AD patients. This progressive loss was closely associated with increased retinal vascular amyloidosis and predicted cerebral amyloid angiopathy scores. These studies brought excitement to the field of retinal exploration in AD. Indeed, many questions still remain open, such as queries related to the temporal progression of AD-related pathology in the retina compared to the brain, the relations between retinal and cerebral changes and whether retinal signs can predict cognitive decline. The extent to which AD affects the retina, including the susceptibility of certain topographical regions and cell types, is currently under intense investigation. Advances in retinal amyloid imaging, hyperspectral imaging, optical coherence tomography, and OCT-angiography encourage the use of such modalities to achieve more accurate, patient- and user-friendly, noninvasive detection and monitoring of AD. In this review, we summarize the current status in the field while addressing the many unknowns regarding Alzheimer's retinopathy.

Retinal changes in Alzheimer's disease- integrated prospects of imaging, functional and molecular advances

Alzheimer's Disease (AD) is a devastating neurodegenerative disorder of the brain, clinically characterised by cognitive deficits that gradually worsen over time. There is, at present, no established cure, or disease-modifying treatments for AD. As life expectancy increases globally, the number of individuals suffering from the disease is projected to increase substantially. Cumulative evidence indicates that AD neuropathological process is initiated several years, if not decades, before clinical signs are evident in patients, and diagnosis made. While several imaging, cognitive, CSF and blood-based biomarkers have been proposed for the early detection of AD; their sensitivity and specificity in the symptomatic stages is highly variable and it is difficult to justify their use in even earlier, pre-clinical stages of the disease. Research has identified potentially measurable functional, structural, metabolic and vascular changes in the retina during early stages of AD. Retina offers a distinctively accessible insight into brain pathology and current and developing ophthalmic technologies have provided us with the possibility of detecting and characterising subtle, disease-related changes. Recent human and animal model studies have further provided mechanistic insights into the biochemical pathways that are altered in the retina in disease, including amyloid and tau deposition. This information coupled with advances in molecular imaging has allowed attempts to monitor biochemical changes and protein aggregation pathology in the retina in AD. This review summarises the existing knowledge that informs our understanding of the impact of AD on the retina and highlights some of the gaps that need to be addressed. Future research will integrate molecular imaging innovation with functional and structural changes to enhance our knowledge of the AD pathophysiological mechanisms and establish the utility of monitoring retinal changes as a potential biomarker for AD.

Retinal involvement in Alzheimer's disease (AD): evidence and current progress on the non-invasive diagnosis and monitoring of AD-related pathology using the eye

Alzheimer's disease (AD) is a common form of age-related dementia that mostly affects the aging population. Clinically, it is a disease characterized by impaired memory and progressive cognitive decline. Although the pathological hallmarks of AD have been traditionally described with a general confinement in the brain, recent studies have shown similar pathological changes in the retina, which is a developmental outgrowth of the forebrain. These AD-related neurodegenerative changes in the retina have been implicated to cause early visual problems in AD even before cognitive impairment becomes apparent. With recent advances in research, the commonly held view that AD-related cerebral pathology causes visual dysfunction through disruption of central visual pathways has been re-examined. Currently, several studies have already explored how AD manifests in the retina and the possibility of using the same retina as a window to non-invasively examine AD-related pathology in the brain. Non-invasive screening of AD through the retina has the potential to improve on early detection and management of the disease since the majority of AD cases are usually diagnosed very late. The purpose of this review is to provide evidence on the involvement of the retina in AD and to suggest a possible direction for future research into the non-invasive screening, diagnosis, and monitoring of AD using the retina.

Hyperspectral Imaging and the Retina: Worth the Wave?

Purpose

Hyperspectral imaging is gaining attention in the biomedical field because it generates additional spectral information to study physiological and clinical processes. Several technologies have been described; however an independent, systematic literature overview is lacking, especially in the field of ophthalmology. This investigation is the first to systematically overview scientific literature specifically regarding retinal hyperspectral imaging.

Methods

A systematic literature review was conducted, in accordance with PRISMA Statement 2009 criteria, in four bibliographic databases: Medline, Embase, Cochrane Database of Systematic Reviews, and Web of Science.

Results

Fifty-six articles were found that meet the review criteria. A range of techniques was reported: Fourier analysis, liquid crystal tunable filters, tunable laser sources, dual-slit monochromators, dispersive prisms and gratings, computed tomography, fiber optics, and Fabry-Perrot cavity filter covered complementary metal oxide semiconductor. We present a narrative synthesis and summary tables of findings of the included articles, because methodologic heterogeneity and diverse research topics prevented a meta-analysis being conducted.

Conclusions

Application in ophthalmology is still in its infancy. Most previous experiments have been performed in the field of retinal oximetry, providing valuable information in the diagnosis and monitoring of various ocular diseases. To date, none of these applications have graduated to clinical practice owing to the lack of sufficiently large validation studies.

Translational Relevance

Given the promising results that smaller studies show for hyperspectral imaging (e.g., in Alzheimer's disease), advanced research in larger validation studies is warranted to determine its true clinical potential.