Microvascular network alterations in the retina of patients with Alzheimer's disease

Measures of retinal health successfully capture risk for Alzheimer's disease and related dementias at midlife

BACKGROUND

Identification of at-risk individuals who would benefit from early intervention for Alzheimer's disease and related dementias (ADRD) is critical as new treatments are developed. Measures of retinal health could offer accessible and low-cost indication of pre-morbid disease risk, but their association with ADRD risk is unknown.

OBJECTIVE

To determine whether midlife retinal neuronal and microvascular measures are associated with ADRD risk-index scores and individual domains of ADRD risk.

METHODS

Data were from the Dunedin Multidisciplinary Health and Development Study, a population-representative longitudinal New Zealand-based birth cohort study. 94.1% (N = 938) of living Study members were seen at age 45 (2017-2019). Retinal neuronal (retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL)) and microvascular (arterioles and venules) measures were used as predictors. Outcome measures were four top ADRD risk indexes (CAIDE, LIBRA, Lancet, and ADU-ADRI), and a comprehensive midlife ADRD risk index, the DunedinARB.

RESULTS

Poorer retinal microvascular health (narrower arterioles and wider venules) was associated with greater ADRD risk (βs = 0.16-0.31; ps < 0.001). Thinner RNFL was modestly associated with higher ADRD risk (βs = 0.05-0.08; ps = 0.02-0.13). Follow-up tests of distinct domains of ADRD risk indicated that while RNFL associations reflected cardiometabolic risk only, microvascular measures were associated with diverse ADRD risk factors.

CONCLUSIONS

Measures of retinal health, particularly microvascular measures, successfully capture ADRD risk across several domains of known risk factors, even at the young midlife age of 45 years. Retinal microvascular imaging may be an accessible, scalable, and relatively low-cost method of assessing ADRD risk among middle-aged adults.

Artificial intelligence in stroke risk assessment and management via retinal imaging

Retinal imaging, used for assessing stroke-related retinal changes, is a non-invasive and cost-effective method that can be enhanced by machine learning and deep learning algorithms, showing promise in early disease detection, severity grading, and prognostic evaluation in stroke patients. This review explores the role of artificial intelligence (AI) in stroke patient care, focusing on retinal imaging integration into clinical workflows. Retinal imaging has revealed several microvascular changes, including a decrease in the central retinal artery diameter and an increase in the central retinal vein diameter, both of which are associated with lacunar stroke and intracranial hemorrhage. Additionally, microvascular changes, such as arteriovenous nicking, increased vessel tortuosity, enhanced arteriolar light reflex, decreased retinal fractals, and thinning of retinal nerve fiber layer are also reported to be associated with higher stroke risk. AI models, such as Xception and EfficientNet, have demonstrated accuracy comparable to traditional stroke risk scoring systems in predicting stroke risk. For stroke diagnosis, models like Inception, ResNet, and VGG, alongside machine learning classifiers, have shown high efficacy in distinguishing stroke patients from healthy individuals using retinal imaging. Moreover, a random forest model effectively distinguished between ischemic and hemorrhagic stroke subtypes based on retinal features, showing superior predictive performance compared to traditional clinical characteristics. Additionally, a support vector machine model has achieved high classification accuracy in assessing pial collateral status. Despite this advancements, challenges such as the lack of standardized protocols for imaging modalities, hesitance in trusting AI-generated predictions, insufficient integration of retinal imaging data with electronic health records, the need for validation across diverse populations, and ethical and regulatory concerns persist. Future efforts must focus on validating AI models across diverse populations, ensuring algorithm transparency, and addressing ethical and regulatory issues to enable broader implementation. Overcoming these barriers will be essential for translating this technology into personalized stroke care and improving patient outcomes.

Hyperhomocysteinemia-induced VCID results in visual deficits, reduced neuroinflammation and vascular alterations in the retina

Over recent years, the retina has been increasingly investigated as a potential biomarker for dementia. A number of studies have looked at the effect of Alzheimer's disease (AD) pathology on the retina and the associations of AD with visual deficits. However, while OCT-A has been explored as a biomarker of cerebral small vessel disease (cSVD), studies identifying the specific retinal changes and mechanisms associated with cSVD are lacking. Using our model of hyperhomocysteinemia-induced cSVD, we aimed to identify the effects of cSVD on visual sensitivity and cognition, retinal glial and vascular cells, and neuroinflammatory and cardiovascular gene expression changes. We placed C57Bl6/SJL mice on a HHcy-inducing diet, a model that has been well characterized to have vascular pathologies in the brain similar to pathologic cSVD. After 14 weeks on diet, mice underwent the Visual-Stimuli 4-arm Maze to identify visual deficits. Whole mount retinas were stained for vessels, microglia and astrocytes to identify glial and vascular changes. Finally, neuroinflammatory and cardiovascular gene expression was measured using NanoString's nCounter system. Ultimately, HHcy led to visual changes that specifically affected the reaction to blue and white light, slightly decreased vascular volume and significantly decreased interaction of microglia with the vasculature, as well as downregulation of inflammatory and vascular genes. These changes provide novel insights and reproduce some prior observations. These studies highlight retinal changes in association with cSVD and serve as a precaution when interpreting vision-dependent cognitive testing of cSVD models.

Retinal vascular changes are associated with PET-based biomarkers of Alzheimer's disease: A pilot study

Background

Retina is a non-invasive channel for assessing changes in brain microvasculature, which has been implicated in the pathophysiology of Alzheimer's disease (AD). Previous studies revealed significant relationship between clinically diagnosed AD and retinal vasculature. However, clinical diagnosis has limited sensitivity and specificity, and those investigations were conducted from traditional retinal fundus photographs which only captured a narrow section of the fundus.

Objective

Determining changes in retinal vasculature from larger area of retina between subjects with positron emission tomography (PET) biomarker-confirmed AD compared to controls.

Methods

Participants were recruited from the community and cognitive disorder clinics. Diagnosis of AD was confirmed by significant amyloid-β (Aβ) and tau uptake on PET scan. Retinal vasculature was imaged with ultra-widefield (UWF) scanning laser ophthalmoscopy and a series of vessel parameters were quantified using the semi-automated Singapore I Vessel Assessment (SIVA) software. Statistical analyses were adjusted for age, gender and systolic blood pressure. In addition, arteriole parameters were adjusted against the same measurements in venules, and vice versa.

Results

Out of the 39 patients, 18 had radiologically confirmed AD. These individuals with AD showed significantly smaller arteriolar fractal dimension (p = 0.032) in UWF images and greater venular tortuosity (p = 0.011) in standard fundus images compared with controls. Presence of significant Aβ and tau burden was associated with lower arteriolar caliber (OR 3.857; 95% CI 1.014-14.67; p = 0.048).

Conclusions

Reduction of fractal dimension in retinal arterioles observed in UWF imaging is associated with cerebral Aβ and tau burden in people with biomarker-confirmed AD. Wide field retinal imaging provides an alternative perspective in demonstrating microvascular alterations related to AD in this pilot study.

Color Fundus Photography and Deep Learning Applications in Alzheimer Disease

Objective

To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD).

Patients and Methods

Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation. ADRET is a bidirectional encoder representations from transformers style self-supervised learning convolutional neural network pretrained on a large data set of retinal color photographs from UK Biobank. The models' performance to distinguish AD from non-AD was determined using mean accuracy, sensitivity, specificity, and receiving operating curves. The generated attention heatmaps were analyzed for distinctive features.

Results

The self-supervised ADRET model had superior accuracy when compared with ADVAS, in both UK Biobank (98.27% vs 77.20%; P<.001) and our institutional testing data sets (98.90% vs 94.17%; P=.04). No major differences were noted between the original and binary vessel segmentation and between both eyes vs single-eye models. Attention heatmaps obtained from patients with AD highlighted regions surrounding small vascular branches as areas of highest relevance to the model decision making.

Conclusion

A bidirectional encoder representations from transformers style self-supervised convolutional neural network pretrained on a large data set of retinal color photographs alone can screen symptomatic AD with high accuracy, better than U-Net-pretrained models. To be translated in clinical practice, this methodology requires further validation in larger and diverse populations and integrated techniques to harmonize fundus photographs and attenuate the imaging-associated noise.

Associations Between Retinal Vascular Occlusions and Dementia

BACKGROUND/OBJECTIVES

Retinal vascular occlusions, such as retinal vein occlusion (RVO) and retinal artery occlusion (RAO), are associated with cognitive impairment, including dementia. Our objective was to examine the odds of dementia among patients with retinal vascular occlusion.

METHODS

This cross-sectional study included 474 patients with retinal vascular occlusion and 948 patients without retinal vascular occlusion (comparison group). Patients in the comparison group were age- and sex-matched to those with vascular occlusion. Logistic regression was used to analyze the odds of all-cause dementia, vascular dementia, and Alzheimer's disease after adjusting for demographic, clinical, and ophthalmic covariates. Main outcome measures included the presence of all-cause dementia, vascular dementia, and Alzheimer's disease.

RESULTS

Patients with RVO (n = 413) had increased odds for all-cause dementia (odds ratio (OR) = 2.32; 95% confidence interval (CI): 1.44-3.75; p < 0.001) and vascular dementia (OR = 3.29; 95% CI: 1.41-7.68; p = 0.006) relative to the comparison group. Patients with central RVO (n = 192) (OR = 2.32; 95% CI: 1.19-4.54; p = 0.014) or branch RVO (n = 221) (OR = 2.68; 95% CI: 1.30-5.50; p = 0.007) had increased odds for all-cause dementia relative to the comparison group. Patients with RAO (n = 61) did not have increased odds of all-cause dementia (OR = 1.01; 95% CI: 0.32-3.26; p = 0.983), vascular dementia (OR = 1.54; 95% CI: 0.22-10.81; p = 0.663), or Alzheimer's disease (OR = 0.32; 95% CI: 0.05-2.20; p = 0.244).

CONCLUSIONS

A history of any RVO is associated with increased rates of all-cause dementia and vascular dementia independent of shared cardiovascular risk factors. These associations are not seen with a history of RAO, or between any subtype of vascular occlusions and Alzheimer's disease.

Transport of β-amyloid from brain to eye causes retinal degeneration in Alzheimer's disease

The eye is closely connected to the brain, providing a unique window to detect pathological changes in the brain. In this study, we discovered β-amyloid (Aβ) deposits along the ocular glymphatic system in patients with Alzheimer's disease (AD) and 5×FAD transgenic mouse model. Interestingly, Aβ from the brain can flow into the eyes along the optic nerve through cerebrospinal fluid (CSF), causing retinal degeneration. Aβ is mainly observed in the optic nerve sheath, the neural axon, and the perivascular space, which might represent the critical steps of the Aβ transportation from the brain to the eyes. Aquaporin-4 facilitates the influx of Aβ in brain-eye transport and out-excretion of the retina, and its absence or loss of polarity exacerbates brain-derived Aβ induced damage and visual impairment. These results revealed brain-to-eye Aβ transport as a major contributor to AD retinopathy, highlighting a new therapeutic avenue in ocular and neurodegenerative disease.

Optical coherence tomography angiography of the retina and choroid in systemic diseases

Optical coherence tomography angiography (OCTA) has transformed ocular vascular imaging, revealing microvascular changes linked to various systemic diseases. This review explores its applications in diabetes, hypertension, cardiovascular diseases, and neurodegenerative diseases. While OCTA provides a valuable window into the body's microvasculature, interpreting the findings can be complex. Additionally, challenges exist due to the relative non-specificity of its findings where changes observed in OCTA might not be unique to a specific disease, variations between OCTA machines, the lack of a standardized normative database for comparison, and potential image artifacts. Despite these limitations, OCTA holds immense potential for the future. The review highlights promising advancements like quantitative analysis of OCTA images, integration of artificial intelligence for faster and more accurate interpretation, and multi-modal imaging combining OCTA with other techniques for a more comprehensive characterization of the ocular vasculature. Furthermore, OCTA's potential future role in personalized medicine, enabling tailored treatment plans based on individual OCTA findings, community screening programs for early disease detection, and longitudinal studies tracking disease progression over time is also discussed. In conclusion, OCTA presents a significant opportunity to improve our understanding and management of systemic diseases. Addressing current limitations and pursuing these exciting future directions can solidify OCTA as an indispensable tool for diagnosis, monitoring disease progression, and potentially guiding treatment decisions across various systemic health conditions.

Retinal Microstructural Changes Reflecting Treatment-Associated Cognitive Dysfunction in Patients with Lower-Grade Gliomas

Purpose

To determine whether microstructural retinal changes, tumor features, and apolipoprotein E (APOE) ε4 polymorphism are correlated with clinically detectable treatment-associated cognitive dysfunction (TACD) in patients with lower-grade gliomas.

Design

Cohort study.

Participants and Controls

Sixteen patients with lower-grade glioma at a United States academic ophthalmology department between January 2021 and November 2023. Normal controls were recruited from convenient sampling.

Methods

Montreal Cognitive Assessment (MoCA) scores and retinal changes were assessed in 6-month intervals. Apolipoprotein E genotyping was performed, and tumor details were recorded. Partial least-squares discriminant (PLSD) model was established to evaluate the association between TACD with APOE genotype, ophthalmic, and tumor features.

Main Outcome Measures

The main outcome measure was cognitive status as measured by the MoCA score and analyzed in relation to ophthalmic measurements, tumor features, and APOE genotype.

Results

Median time to first eye examination was 34 months (2-266) from tumor diagnosis and 23 months (0-246) from radiation. Nine patients (56%) had abnormal cognition (MoCA <26/30). Montreal Cognitive Assessment scores were significantly worse in patients with temporal (22 ± 7.2) than frontal lobe tumors (26 ± 3.1, P = 0.02) and those with oligodendrogliomas (22 ± 4.1) than astrocytomas (26 ± 3.6, = 0.02). Patients with TACD had significant radial peripapillary capillary density loss (45% ± 4.6) compared with those with normal cognition (49% ± 2.6, P = 0.02). A PLSD model correlated MoCA scores with retinal nerve fiber thickness, intraocular pressure, foveal avascular zone, best-corrected visual acuity, months since first diagnosis, and tumor pathology (oligodendroglioma or not). Using these features, the model identified patients with TACD with 77% accuracy. Apolipoprotein E genotyping showed: 2 ε2/ε3 (13%), 10 ε3/ε3 (63%), and 1 ε3/ε4 (6%).

Conclusions

Retinal microstructural changes may serve as biomarkers for TACD in patients with lower-grade gliomas. Temporal lobe tumors and oligodendrogliomas may increase susceptibility to TACD. Utilization of retinal markers may enhance TACD diagnosis, progression monitoring, and inform management of lower-grade patients with glioma. A larger study with serial eye examinations is warranted to evaluate the role of APOE ε4 and develop a predictive model.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Retinal Microvasculature Causally Affects the Brain Cortical Structure: A Mendelian Randomization Study

Purpose

To reveal the causality between retinal vascular density (VD), fractal dimension (FD), and brain cortex structure using Mendelian randomization (MR).

Design

Cross-sectional study.

Participants

Genome-wide association studies of VD and FD involving 54 813 participants from the United Kingdom Biobank were used. The brain cortical features, including the cortical thickness (TH) and surface area (SA), were extracted from 51 665 patients across 60 cohorts. Surface area and TH were measured globally and in 34 functional regions using magnetic resonance imaging.

Methods

Bidirectional univariable MR (UVMR) was used to detect the causality between FD, VD, and brain cortex structure. Multivariable MR (MVMR) was used to adjust for confounding factors, including body mass index and blood pressure.

Main Outcome Measures

The global and regional measurements of brain cortical SA and TH.

Results

At the global level, higher VD is related to decreased TH (β = -0.0140 mm, 95% confidence interval: -0.0269 mm to -0.0011 mm, P = 0.0339). At the functional level, retinal FD is related to the TH of banks of the superior temporal sulcus and transverse temporal region without global weighted, as well as the SA of the posterior cingulate after adjustment. Vascular density is correlated with the SA of subregions of the frontal lobe and temporal lobe, in addition to the TH of the inferior temporal, entorhinal, and pars opercularis regions in both UVMR and MVMR. Bidirectional MR studies showed a causation between the SA of the parahippocampal and cauda middle frontal gyrus and retinal VD. No pleiotropy was detected.

Conclusions

Fractal dimension and VD causally influence the cortical structure and vice versa, indicating that the retinal microvasculature may serve as a biomarker for cortex structural changes. Our study provides insights into utilizing noninvasive fundus images to predict cortical structural deteriorations and neuropsychiatric disorders.

Financial Disclosures

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Value proposition of retinal imaging in Alzheimer's disease screening: A review of eight evolving trends

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Current diagnostic modalities of AD generally focus on detecting the presence of amyloid β and tau protein in the brain (for example, positron emission tomography [PET] and cerebrospinal fluid testing), but these are limited by their high cost, invasiveness, and lack of expertise. Retinal imaging exhibits potential in AD screening and risk stratification, as the retina provides a platform for the optical visualization of the central nervous system in vivo, with vascular and neuronal changes that mirror brain pathology. Given the paradigm shift brought by advances in artificial intelligence and the emergence of disease-modifying therapies, this article aims to summarize and review the current literature to highlight 8 trends in an evolving landscape regarding the role and potential value of retinal imaging in AD screening.

Early detection of dementia through retinal imaging and trustworthy AI

Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection.

Atherosclerotic burden and cerebral small vessel disease: exploring the link through microvascular aging and cerebral microhemorrhages

Cerebral microhemorrhages (CMHs, also known as cerebral microbleeds) are a critical but frequently underestimated aspect of cerebral small vessel disease (CSVD), bearing substantial clinical consequences. Detectable through sensitive neuroimaging techniques, CMHs reveal an extensive pathological landscape. They are prevalent in the aging population, with multiple CMHs often being observed in a given individual. CMHs are closely associated with accelerated cognitive decline and are increasingly recognized as key contributors to the pathogenesis of vascular cognitive impairment and dementia (VCID) and Alzheimer's disease (AD). This review paper delves into the hypothesis that atherosclerosis, a prevalent age-related large vessel disease, extends its pathological influence into the cerebral microcirculation, thereby contributing to the development and progression of CSVD, with a specific focus on CMHs. We explore the concept of vascular aging as a continuum, bridging macrovascular pathologies like atherosclerosis with microvascular abnormalities characteristic of CSVD. We posit that the same risk factors precipitating accelerated aging in large vessels (i.e., atherogenesis), primarily through oxidative stress and inflammatory pathways, similarly instigate accelerated microvascular aging. Accelerated microvascular aging leads to increased microvascular fragility, which in turn predisposes to the formation of CMHs. The presence of hypertension and amyloid pathology further intensifies this process. We comprehensively overview the current body of evidence supporting this interconnected vascular hypothesis. Our review includes an examination of epidemiological data, which provides insights into the prevalence and impact of CMHs in the context of atherosclerosis and CSVD. Furthermore, we explore the shared mechanisms between large vessel aging, atherogenesis, microvascular aging, and CSVD, particularly focusing on how these intertwined processes contribute to the genesis of CMHs. By highlighting the role of vascular aging in the pathophysiology of CMHs, this review seeks to enhance the understanding of CSVD and its links to systemic vascular disorders. Our aim is to provide insights that could inform future therapeutic approaches and research directions in the realm of neurovascular health.

AI-based fully automatic analysis of retinal vascular morphology in pediatric high myopia

PURPOSE

To investigate the changes in retinal vascular structures associated with various stages of myopia by designing automated software based on an artificial intelligence model.

METHODS

The study involved 1324 pediatric participants from the National Children's Medical Center in China, and 2366 high-quality retinal images and corresponding refractive parameters were obtained and analyzed. Spherical equivalent refraction (SER) degree was calculated. We proposed a data analysis model based on a combination of the Convolutional Neural Networks (CNN) model and the attention module to classify images, segment vascular structures, and measure vascular parameters, such as main angle (MA), branching angle (BA), bifurcation edge angle (BEA) and bifurcation edge coefficient (BEC). One-way ANOVA compared parameter measurements between the normal fundus, low myopia, moderate myopia, and high myopia groups.

RESULTS

The mean age was 9.85 ± 2.60 years, with an average SER of -1.49 ± 3.16D in the right eye and - 1.48 ± 3.13D in the left eye. There were 279 (12.38%) images in the normal group and 384 (16.23%) images in the high myopia group. Compared with normal fundus, the MA of fundus vessels in different myopic refractive groups was significantly reduced (P = 0.006, P = 0.004, P = 0.019, respectively), and the performance of the venous system was particularly obvious (P < 0.001). At the same time, the BEC decreased disproportionately (P < 0.001). Further analysis of fundus vascular parameters at different degrees of myopia showed that there were also significant differences in BA and branching coefficient (BC). The arterial BA value of the fundus vessel in the high myopia group was lower than that of other groups (P = 0.032, 95% confidence interval [CI], 0.22-4.86), while the venous BA values increased (P = 0.026). The BEC values of high myopia were higher than those of low and moderate myopia groups. When the loss function of our data classification model converged to 0.09, the model accuracy reached 94.19%.

CONCLUSION

The progression of myopia is associated with a series of quantitative retinal vascular parameters, particularly the vascular angles. As the degree of myopia increases, the diversity of vascular characteristics represented by these parameters also increases.

Unlocking the Potential of Vessel Density and the Foveal Avascular Zone in Optical Coherence Tomography Angiography as Biomarkers in Alzheimer's Disease

Alzheimer's disease is the most prevalent form of dementia. Apart from its traditional clinical diagnostic methods, novel ocular imaging biomarkers have the potential to significantly enhance the diagnosis of Alzheimer's disease. Ophthalmologists might be able to play a crucial role in this multidisciplinary approach, aiding in the early detection and diagnosis of Alzheimer's disease through the use of advanced retinal imaging techniques. This systematic literature review the utilization of optical coherence tomography angiography biomarkers, specifically vessel density and the foveal avascular zone, for the diagnosis of Alzheimer's disease. A comprehensive search was performed across multiple academic journal databases, including 11 relevant studies. The selected studies underwent thorough analysis to assess the potential of these optical coherence tomography angiography biomarkers as diagnostic tools for Alzheimer's disease. The assessment of vessel density and the foveal avascular zone have emerged as a promising avenue for identifying and diagnosing Alzheimer's disease. However, it is imperative to acknowledge that further targeted investigations are warranted to address the inherent limitations of the existing body of literature. These limitations encompass various factors such as modest sample sizes, heterogeneity among study populations, disparities in optical coherence tomography angiography imaging protocols, and inconsistencies in the reported findings. In order to establish the clinical utility and robustness of these biomarkers in Alzheimer's disease diagnosis, future research endeavors should strive to overcome these limitations by implementing larger-scale studies characterized by standardized protocols and comprehensive assessments.

Retinal Vascular and Structural Changes in the Murine Alzheimer's APPNL-F/NL-F Model from 6 to 20 Months

Alzheimer's disease (AD) may manifest retinal changes preceding brain pathology. A transversal case-control study utilized spectral-domain OCT angiography (SD-OCTA) and Angio-Tool software 0.6a to assess retinal vascular structures and OCT for inner and outer retina thickness in the APPNL-F/NL-F AD model at 6, 9, 12, 15, 17, and 20 months old. Comparisons to age-matched wild type (WT) were performed. The analysis focused on the three vascular plexuses using AngiooTool and on retinal thickness, which was represented with the Early Treatment Diabetic Retinopathy Study (ETDRS) sectors. Compared to WT, the APPNL-F/NL-F group exhibited both vascular and structural changes as early as 6 months persisting and evolving at 15, 17, and 20 months. Significant vascular alterations, principally in the superficial vascular complex (SVC), were observed. There was a significant decrease in the vessel area and the total vessel length in SVC, intermediate, and deep capillary plexus. The inner retina in the APPNL-F/NL-F group predominantly decreased in thickness while the outer retina showed increased thickness in most analyzed time points compared to the control group. There are early vascular and structural retinal changes that precede the cognitive changes, which appear at later stages. Therefore, the natural history of the APPNL-F/NL-F model may be more similar to human AD than other transgenic models.

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.

Progress and challenges of in vivo flow cytometry and its applications in circulating cells of eyes

Circulating inflammatory cells in eyes have emerged as early indicators of numerous major diseases, yet the monitoring of these cells remains an underdeveloped field. In vivo flow cytometry (IVFC), a noninvasive technique, offers the promise of real-time, dynamic quantification of circulating cells. However, IVFC has not seen extensive applications in the detection of circulating cells in eyes, possibly due to the eye's unique physiological structure and fundus imaging limitations. This study reviews the current research progress in retinal flow cytometry and other fundus examination techniques, such as adaptive optics, ultra-widefield retinal imaging, multispectral imaging, and optical coherence tomography, to propose novel ideas for circulating cell monitoring.

Choriocapillaris reduction accurately discriminates against early-onset Alzheimer's disease

INTRODUCTION

This study addresses the urgent need for non-invasive early-onset Alzheimer's disease (EOAD) prediction. Using optical coherence tomography angiography (OCTA), we present a choriocapillaris model sensitive to EOAD, correlating with serum biomarkers.

METHODS

Eighty-four EOAD patients and 73 controls were assigned to swept-source OCTA (SS-OCTA) or the spectral domain OCTA (SD-OCTA) cohorts. Our hypothesis on choriocapillaris predictive potential in EOAD was tested and validated in these two cohorts.

RESULTS

Both cohorts revealed diminished choriocapillaris signals, demonstrating the highest discriminatory capability (area under the receiver operating characteristic curve: SS-OCTA 0.913, SD-OCTA 0.991; P < 0.001). A sparser SS-OCTA choriocapillaris correlated with increased serum amyloid beta (Aβ)42, Aβ42/40, and phosphorylated tau (p-tau)181 levels (all P < 0.05). Apolipoprotein E status did not affect choriocapillaris measurement.

DISCUSSION

The choriocapillaris, observed in both cohorts, proves sensitive to EOAD diagnosis, and correlates with serum Aβ and p-tau181 levels, suggesting its potential as a diagnostic tool for identifying and tracking microvascular changes in EOAD.

HIGHLIGHTS

Optical coherence tomography angiography may be applied for non-invasive screening of Alzheimer's disease (AD). Choriocapillaris demonstrates high sensitivity and specificity for early-onset AD diagnosis. Microvascular dynamics abnormalities are associated with AD.

The PREVENT dementia programme: baseline demographic, lifestyle, imaging and cognitive data from a midlife cohort study investigating risk factors for dementia

PREVENT is a multi-centre prospective cohort study in the UK and Ireland that aims to examine midlife risk factors for dementia and identify and describe the earliest indices of disease development. The PREVENT dementia programme is one of the original epidemiological initiatives targeting midlife as a critical window for intervention in neurodegenerative conditions. This paper provides an overview of the study protocol and presents the first summary results from the initial baseline data to describe the cohort. Participants in the PREVENT cohort provide demographic data, biological samples (blood, saliva, urine and optional cerebrospinal fluid), lifestyle and psychological questionnaires, undergo a comprehensive cognitive test battery and are imaged using multi-modal 3-T MRI scanning, with both structural and functional sequences. The PREVENT cohort governance structure is described, which includes a steering committee, a scientific advisory board and core patient and public involvement groups. A number of sub-studies that supplement the main PREVENT cohort are also described. The PREVENT cohort baseline data include 700 participants recruited between 2014 and 2020 across five sites in the UK and Ireland (Cambridge, Dublin, Edinburgh, London and Oxford). At baseline, participants had a mean age of 51.2 years (range 40-59, SD ± 5.47), with the majority female (n = 433, 61.9%). There was a near equal distribution of participants with and without a parental history of dementia (51.4% versus 48.6%) and a relatively high prevalence of APOEɛ4 carriers (n = 264, 38.0%). Participants were highly educated (16.7 ± 3.44 years of education), were mainly of European Ancestry (n = 672, 95.9%) and were cognitively healthy as measured by the Addenbrookes Cognitive Examination-III (total score 95.6 ± 4.06). Mean white matter hyperintensity volume at recruitment was 2.26 ± 2.77 ml (median = 1.39 ml), with hippocampal volume being 8.15 ± 0.79 ml. There was good representation of known dementia risk factors in the cohort. The PREVENT cohort offers a novel data set to explore midlife risk factors and early signs of neurodegenerative disease. Data are available open access at no cost via the Alzheimer's Disease Data Initiative platform and Dementia Platforms UK platform pending approval of the data access request from the PREVENT steering group committee.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

BACKGROUND

Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

MAIN TEXT

This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care.

CONCLUSION

AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models

Purpose

Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results.

Methods

In this review, we systematically report studies with datasets of retinal images from patients with neurodegenerative diseases, including Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, and others. We also review and characterize the models in the current literature which have been used for classification, regression, or segmentation problems using retinal images in patients with neurodegenerative diseases.

Results

Our review found several existing datasets and models with various imaging modalities primarily in patients with Alzheimer's disease, with most datasets on the order of tens to a few hundred images. We found limited data available for the other neurodegenerative diseases. Although cross-sectional imaging data for Alzheimer's disease is becoming more abundant, datasets with longitudinal imaging of any disease are lacking.

Conclusions

The use of bilateral and multimodal imaging together with metadata seems to improve model performance, thus multimodal bilateral image datasets with patient metadata are needed. We identified several deep learning tools that have been useful in this context including feature extraction algorithms specifically for retinal images, retinal image preprocessing techniques, transfer learning, feature fusion, and attention mapping. Importantly, we also consider the limitations common to these models in real-world clinical applications.

Translational Relevance

This systematic review evaluates the deep learning models and retinal features relevant in the evaluation of retinal images of patients with neurodegenerative disease.

Association of fractal dimension and other retinal vascular network parameters with cognitive performance and neuroimaging biomarkers: The Multi-Ethnic Study of Atherosclerosis (MESA)

INTRODUCTION

Retinal vascular network changes may reflect the integrity of the cerebral microcirculation, and may be associated with cognitive impairment.

METHODS

Associations of retinal vascular measures with cognitive function and MRI biomarkers were examined amongst Multi-Ethnic Study of Atherosclerosis (MESA) participants in North Carolina who had gradable retinal photographs at Exams 2 (2002 to 2004, n = 313) and 5 (2010 to 2012, n = 306), and detailed cognitive testing and MRI at Exam 6 (2016 to 2018).

RESULTS

After adjustment for covariates and multiple comparisons, greater arteriolar fractal dimension (FD) at Exam 2 was associated with less isotropic free water of gray matter regions (β = -0.0005, SE = 0.0024, p = 0.01) at Exam 6, while greater arteriolar FD at Exam 5 was associated with greater gray matter cortical volume (in mm3 , β = 5458, SE = 20.17, p = 0.04) at Exam 6.

CONCLUSION

Greater arteriolar FD, reflecting greater complexity of the branching pattern of the retinal arteries, is associated with MRI biomarkers indicative of less neuroinflammation and neurodegeneration.

Retinal Vascular Measurements and Mortality Risk: Evidence From the UK Biobank Study

Purpose

This study aimed to investigate the association between quantitative retinal vascular measurements and the risk of all-cause and premature mortality.

Methods

In this population-based cohort study using the UK Biobank data, we employed the Retina-based Microvascular Health Assessment System to assess fundus images for image quality and extracted 392 retinal vascular measurements per fundus image. These measurements encompass six categories of vascular features: caliber, density, length, tortuosity, branching angle, and complexity. Univariate Cox regression models were used to identify potential indicators of mortality risk using data on all-cause and premature mortality from death registries. Multivariate Cox regression models were then used to test these associations while controlling for confounding factors.

Results

The final analysis included 66,415 participants. After adjusting for demographic, health, and lifestyle factors and genetic risk score, 18 and 10 retinal vascular measurements were significantly associated with all-cause mortality and premature mortality, respectively. In the fully adjusted model, the following measurements of different vascular features were significantly associated with all-cause mortality and premature mortality: arterial bifurcation density (branching angle), number of arterial segments (complexity), interquartile range and median absolute deviation of arterial curve angle (tortuosity), mean and median values of mean pixel widths of all arterial segments in each image (caliber), skeleton density of arteries in macular area (density), and minimum venular arc length (length).

Conclusions

The study revealed 18 retinal vascular measurements significantly associated with all-cause mortality and 10 associated with premature mortality. Those identified parameters should be further studied for biological mechanisms connecting them to increased mortality risk.

Translational Relevance

This study identifies retinal biomarkers for increased mortality risk and provides novel targets for investigating the underlying biological mechanisms.

Ultra-Widefield Imaging of the Retinal Macrovasculature in Parkinson Disease Versus Controls With Normal Cognition Using Alpha-Shapes Analysis

Purpose

To investigate retinal vascular characteristics using ultra-widefield (UWF) scanning laser ophthalmoscopy in Parkinson disease (PD).

Methods

Individuals with an expert-confirmed clinical diagnosis of PD and controls with normal cognition without PD underwent Optos California UWF imaging. Patients with diabetes, uncontrolled hypertension, glaucoma, dementia, other movement disorders, or known retinal or optic nerve pathology were excluded. Images were analyzed using Vasculature Assessment and Measurement Platform for Images of the Retina (VAMPIRE-UWF) software, which describes retinal vessel width gradient and tortuosity, provides vascular network fractal dimensions, and conducts alpha-shape analysis to further characterize vascular morphology (complexity, Opαmin; spread, OpA).

Results

In the PD cohort, 53 eyes of 38 subjects were assessed; in the control cohort, 51 eyes of 33 subjects were assessed. Eyes with PD had more tortuous retinal arteries in the superotemporal quadrant (P = 0.043). In eyes with PD, alpha-shape analysis revealed decreased OpA, indicating less retinal vasculature spread compared to controls (P = 0.032). Opαmin was decreased in PD (P = 0.044), suggesting increased vascular network complexity. No differences were observed in fractal dimension in any region of interest.

Conclusions

This pilot study suggests that retinal vasculature assessment on UWF images using alpha-shape analysis reveals differences in retinal vascular network spread and complexity in PD and may be a more sensitive metric compared to fractal dimension.

Translational Relevance

Retinal vasculature assessment using these novel methods may be useful in understanding ocular manifestations of PD and the development of retinal biomarkers.

Choriocapillaris and Retinal Vascular Alterations in Presymptomatic Alzheimer's Disease

Purpose

To compare optical coherence tomography angiography (OCTA) retina metrics between cognitively healthy subjects with pathological versus normal cerebrospinal fluid (CSF) Aβ42/tau ratios.

Methods

Swept-source OCTA scans were collected using the Zeiss PLEX Elite 9000 and analyzed on 23 cognitively healthy (CH) subjects who had previously undergone CSF analysis. Thirteen subjects had a pathological Aβ42/tau (PAT) ratio of <2.7132, indicative of presymptomatic Alzheimer's disease (AD), and 10 had a normal Aβ42/tau (NAT) ratio of ≥2.7132. OCTA en face images of the superficial vascular complex (SVC) and deep vascular complex were binarized and skeletonized to quantify the perfusion density (PD), vessel length density (VLD), and fractal dimension (FrD). The foveal avascular zone (FAZ) area was calculated using the SVC slab. Choriocapillaris flow deficits (CCFDs) were computed from the en face OCTA slab of the CC. The above parameters were compared between CH-PATs and CH-NATs.

Results

Compared to CH-NATs, CH-PATs showed significantly decreased PD, VLD, and FrD in the SVC, with a significantly increased FAZ area and CCFDs.

Conclusions

Swept-source OCTA analysis of the SVC and CC suggests a significant vascular loss at the CH stage of pre-AD that might be an indicator of a neurodegenerative process initiated by the impaired clearance of Aβ42 in the blood vessel wall and by phosphorylated tau accumulation in the perivascular spaces, a process that most likely mirrors that in the brain. If confirmed in larger longitudinal studies, OCTA retinal and inner choroidal metrics may be important biomarkers for assessing presymptomatic AD.

Semi-supervised point consistency network for retinal artery/vein classification

Recent deep learning methods with convolutional neural networks (CNNs) have boosted advance prosperity of medical image analysis and expedited the automatic retinal artery/vein (A/V) classification. However, it is challenging for these CNN-based approaches in two aspects: (1) specific tubular structures and subtle variations in appearance, contrast, and geometry, which tend to be ignored in CNNs with network layer increasing; (2) limited well-labeled data for supervised segmentation of retinal vessels, which may hinder the effectiveness of deep learning methods. To address these issues, we propose a novel semi-supervised point consistency network (SPC-Net) for retinal A/V classification. SPC-Net consists of an A/V classification (AVC) module and a multi-class point consistency (MPC) module. The AVC module adopts an encoder-decoder segmentation network to generate the prediction probability map of A/V for supervised learning. The MPC module introduces point set representations to adaptively generate point set classification maps of the arteriovenous skeleton, which enjoys its prediction flexibility and consistency (i.e. point consistency) to effectively alleviate arteriovenous confusion. In addition, we propose a consistency regularization between the predicted A/V classification probability maps and point set representations maps for unlabeled data to explore the inherent segmentation perturbation of the point consistency, reducing the need for annotated data. We validate our method on two typical public datasets (DRIVE, HRF) and a private dataset (TR280) with different resolutions. Extensive qualitative and quantitative experimental results demonstrate the effectiveness of our proposed method for supervised and semi-supervised learning.

Eye Adult Changes in Thought (Eye ACT) Study: Design and Report on the Inaugural Cohort

Background

Conflicting research on retinal biomarkers of Alzheimer's disease and related dementias (AD/ADRD) is likely related to limited sample sizes, study design, and protocol differences.

Objective

The prospective Eye Adult Changes in Thought (Eye ACT) seeks to address these gaps.

Methods

Eye ACT participants are recruited from ACT, an ongoing cohort of dementia-free, older adults followed biennially until AD/ADRD, and undergo visual function and retinal imaging assessment either in clinic or at home.

Results

330 participants were recruited as of 03/2023. Compared to ACT participants not in Eye ACT (N = 1868), Eye ACT participants (N = 330) are younger (mean age: 70.3 versus 71.2, p = 0.014), newer to ACT (median ACT visits since baseline: 3 versus 4, p < 0.001), have more years of education (17.7 versus 16.2, p < 0.001) and had lower rates of visual impairment (12% versus 22%, p < 0.001). Compared to those seen in clinic (N = 300), Eye ACT participants seen at home (N = 30) are older (77.2 versus 74.9, p = 0.015), more frequently female (60% versus 49%, p = 0.026), and have significantly worse visual acuity (71.1 versus 78.9 Early Treatment Diabetic Retinopathy Study letters, p < 0.001) and contrast sensitivity (-1.9 versus -2.1 mean log units at 3 cycles per degree, p = 0.002). Cognitive scores and retinal imaging measurements are similar between the two groups.

Conclusions

Participants assessed at home had significantly worse visual function than those seen in clinic. By including these participants, Eye ACT provides a unique longitudinal cohort for evaluating potential retinal biomarkers of dementia.

Bridging Retinal and Cerebral Neurodegeneration: A Focus on Crosslinks between Alzheimer-Perusini's Disease and Retinal Dystrophies

In the early stages of Alzheimer-Perusini's disease (AD), individuals often experience vision-related issues such as color vision impairment, reduced contrast sensitivity, and visual acuity problems. As the disease progresses, there is a connection with glaucoma and age-related macular degeneration (AMD) leading to retinal cell death. The retina's involvement suggests a link with the hippocampus, where most AD forms start. A thinning of the retinal nerve fiber layer (RNFL) due to the loss of retinal ganglion cells (RGCs) is seen as a potential AD diagnostic marker using electroretinography (ERG) and optical coherence tomography (OCT). Amyloid beta fragments (Aβ), found in the eye's vitreous and aqueous humor, are also present in the cerebrospinal fluid (CSF) and accumulate in the retina. Aβ is known to cause tau hyperphosphorylation, leading to its buildup in various retinal layers. However, diseases like AD are now seen as mixed proteinopathies, with deposits of the prion protein (PrP) and α-synuclein found in affected brains and retinas. Glial cells, especially microglial cells, play a crucial role in these diseases, maintaining immunoproteostasis. Studies have shown similarities between retinal and brain microglia in terms of transcription factor expression and morphotypes. All these findings constitute a good start to achieving better comprehension of neurodegeneration in both the eye and the brain. New insights will be able to bring the scientific community closer to specific disease-modifying therapies.

Retinal microvascular complexity as a putative biomarker of biological age: a pilot study

Physiological changes associated with aging increase the risk for the development of age-related diseases. This increase is non-specific to the type of age-related disease, although each disease develops through a unique pathophysiologic mechanism. People who age at a faster rate develop age-related diseases earlier in their life. They have an older "biological age" compared to their "chronological age". Early detection of individuals with accelerated aging would allow timely intervention to postpone the onset of age-related diseases. This would increase their life expectancy and their length of good quality life. The goal of this study was to investigate whether retinal microvascular complexity could be used as a biomarker of biological age. Retinal images of 68 participants ages ranging from 19 to 82 years were collected in an observational cross-sectional study. Twenty of the old participants had age-related diseases such as hypertension, type 2 diabetes, and/or Alzheimer's dementia. The rest of the participants were healthy. Retinal images were captured by a hand-held, non-mydriatic fundus camera and quantification of the microvascular complexity was performed by using Sholl's, box-counting fractal, and lacunarity analysis. In the healthy subjects, increasing chronological age was associated with lower retinal microvascular complexity measured by Sholl's analysis. Decreased box-counting fractal dimension was present in old patients, and this decrease was 2.1 times faster in participants who had age-related diseases (p = 0.047). Retinal microvascular complexity could be a promising new biomarker of biological age. The data from this study is the first of this kind collected in Montenegro. It is freely available for use.

Exposome and unhealthy aging: environmental drivers from air pollution to occupational exposures

The aging population worldwide is facing a significant increase in age-related non-communicable diseases, including cardiovascular and brain pathologies. This comprehensive review paper delves into the impact of the exposome, which encompasses the totality of environmental exposures, on unhealthy aging. It explores how environmental factors contribute to the acceleration of aging processes, increase biological age, and facilitate the development and progression of a wide range of age-associated diseases. The impact of environmental factors on cognitive health and the development of chronic age-related diseases affecting the cardiovascular system and central nervous system is discussed, with a specific focus on Alzheimer's disease, Parkinson's disease, stroke, small vessel disease, and vascular cognitive impairment (VCI). Aging is a major risk factor for these diseases. Their pathogenesis involves cellular and molecular mechanisms of aging such as increased oxidative stress, impaired mitochondrial function, DNA damage, and inflammation and is influenced by environmental factors. Environmental toxicants, including ambient particulate matter, pesticides, heavy metals, and organic solvents, have been identified as significant contributors to cardiovascular and brain aging disorders. These toxicants can inflict both macro- and microvascular damage and many of them can also cross the blood-brain barrier, inducing neurotoxic effects, neuroinflammation, and neuronal dysfunction. In conclusion, environmental factors play a critical role in modulating cardiovascular and brain aging. A deeper understanding of how environmental toxicants exacerbate aging processes and contribute to the pathogenesis of neurodegenerative diseases, VCI, and dementia is crucial for the development of preventive strategies and interventions to promote cardiovascular, cerebrovascular, and brain health. By mitigating exposure to harmful environmental factors and promoting healthy aging, we can strive to reduce the burden of age-related cardiovascular and brain pathologies in the aging population.

Potential Retinal Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD) represents a major diagnostic challenge, as early detection is crucial for effective intervention. This review examines the diagnostic challenges facing current AD evaluations and explores the emerging field of retinal alterations as early indicators. Recognizing the potential of the retina as a noninvasive window to the brain, we emphasize the importance of identifying retinal biomarkers in the early stages of AD. However, the examination of AD is not without its challenges, as the similarities shared with other retinal diseases introduce complexity in the search for AD-specific markers. In this review, we address the relevance of using the retina for the early diagnosis of AD and the complex challenges associated with the search for AD-specific retinal biomarkers. We provide a comprehensive overview of the current landscape and highlight avenues for progress in AD diagnosis by retinal examination.

The association between retinal vascular fractal dimension and cognitive function in the community-dwelling older adults cohort TIGER

BACKGROUND/PURPOSE

The small retinal vessels reflect cerebral microcirculation and its fractal dimension (Df), representing the complexity of the retinal microcirculation. However, the connection between retinal circulation and cognitive function lacked consistent and longitudinal evidence. This study aimed to explore the association between retinal vascular complexity and cognitive impairment over time in non-demented community-dwelling older adults.

METHODS

This four-year prospective cohort study (2015-2019) is part of the ongoing Taiwan Initiative for Geriatric Epidemiological Research (TIGER, 2011 to present). Of the 434 older adults (age >65) recruited, 207 participants were included for analysis. The retinal vascular Df was assessed by baseline images from fundus photography (2015-2017). Global (Montreal Cognitive Assessment-Taiwanese version, MoCA-T) and domain-specific cognition were assessed at the baseline and 2-year follow-up (2017-2019). The multivariable linear regression models and generalized linear mixed models were used to evaluate the association of Df with cognitive decline/impairment over time.

RESULTS

Decreased left retinal vascular complexity was associated with poor attention performance (β = -0.40). As follow-up time increased, decreased vascular complexity was associated with poor memory performance (right: β = -0.25; left: β = -0.19), and decreased right vascular complexity was associated with poor attention performance (β = -0.18).

CONCLUSION

Low retinal vascular complexity of the right or left eye may be differentially associated with cognitive domains in community-dwelling older adults over two years. The retinal vascular Df of either eye may be served as a screening tool for detecting cognitive impairment in the preclinical phase of dementia.

Retinal microvasculature and incident dementia over 10 years: The Three-City-Alienor cohort

Introduction

We explored the longitudinal relationship between retinal vascular features and dementia incidence over 10 years.

Methods

Among 584 participants from the Three-City-Alienor (3C-Alienor) population-based cohort, quantitative retinal vascular features (caliber, tortuosity, fractal dimension) were measured using semi-automated software. Dementia was actively diagnosed over the follow-up period.

Results

One hundred twenty-eight participants (21.9%) developed dementia over a median of 7.1 years. In Cox proportional hazards models adjusted for sociodemographic characteristics, apolipoprotein E (APOE) ε4, and vascular factors, increased retinal arteriolar tortuosity was associated with all-cause dementia (hazard ratio per standard deviation increase, 1.21; 95% confidence interval: 1.02-1.44). Wider retinal calibers and a higher venular tortuosity were associated with mixed/vascular dementia, but not Alzheimer's disease. Fractal dimensions were not associated with dementia.

Discussion

Changes in the retinal microvasculature were associated with dementia risk. More studies are needed to replicate these findings and determine which features might help identify persons at risk at an early stage.

HIGHLIGHTS

The retinal microvasculature might reflect the brain microvasculatureWe explored the association between retinal vascular features and incident dementia584 participants from the Three-City-Alienor cohort were followed-up over 10 yearsIncreased arteriolar tortuosity and venular calibers were associated with dementia riskRetinal imaging might help identify persons at risk of future dementia.

Frequency of coexistent eye diseases and cognitive impairment or dementia: a systematic review and meta-analysis

OBJECTIVE

We aim to quantify the co-existence of age-related macular degeneration (AMD), glaucoma, or diabetic retinopathy (DR) and cognitive impairment or dementia.

METHOD

MEDLINE, EMBASE, PsycINFO and CINAHL were searched (to June 2020). Observational studies reporting incidence or prevalence of AMD, glaucoma, or DR in people with cognitive impairment or dementia, and of cognitive impairment or dementia among people with AMD, glaucoma, or DR were included.

RESULTS

Fifty-six studies (57 reports) were included but marked by heterogeneities in the diagnostic criteria or definitions of the diseases, study design, and case mix. Few studies reported on the incidence. Evidence was sparse but consistent in individuals with mild cognitive impairment where 7.7% glaucoma prevalence was observed. Prevalence of AMD and DR among people with cognitive impairment ranged from 3.9% to 9.4% and from 11.4% to 70.1%, respectively. Prevalence of AMD and glaucoma among people with dementia ranged from 1.4 to 53% and from 0.2% to 25.9%, respectively. Prevalence of DR among people with dementia was 11%. Prevalence of cognitive impairment in people with AMD, glaucoma, and DR ranged from 8.4% to 52.4%, 12.3% to 90.2%, and 3.9% to 77.8%, respectively, and prevalence of dementia in people with AMD, glaucoma and DR ranged from 9.9% to 62.6%, 2.5% to 3.3% and was 12.5%, respectively.

CONCLUSIONS

Frequency of comorbid eye disease and cognitive impairment or dementia varied considerably. While more population-based estimations of the co-existence are needed, interdisciplinary collaboration might be helpful in the management of these conditions to meet healthcare needs of an ageing population.

TRIAL REGISTRATION

PROSPERO registration: CRD42020189484.

Vascular cognitive impairment - Past, present, and future challenges

Vascular cognitive impairment (VCI) is a lifelong process encompassing a broad spectrum of cognitive disorders, ranging from subtle or mild deficits to prodromal and fully developed dementia, originating from cerebrovascular lesions such as large and small vessel disease. Genetic predisposition and environmental exposure to risk factors such as unhealthy lifestyles, hypertension, cardiovascular disease, and metabolic disorders will synergistically interact, yielding biochemical and structural brain changes, ultimately culminating in VCI. However, little is known about the pathological processes underlying VCI and the temporal dynamics between risk factors and disease mechanisms (biochemical and structural brain changes). This narrative review aims to provide an evidence-based summary of the link between individual vascular risk/disorders and cognitive dysfunction and the potential structural and biochemical pathophysiological processes. We also discuss some key challenges for future research on VCI. There is a need to shift from individual risk factors/disorders to comorbid vascular burden, identifying and integrating imaging and fluid biomarkers, implementing a life-course approach, considering possible neuroprotective influences of positive life exposures, and addressing biological sex at birth and gender differences. Finally, this review highlights the need for future researchers to leverage and integrate multidimensional data to advance our understanding of the mechanisms and pathophysiology of VCI.

Look into my eyes: What can eye-based measures tell us about the relationship between physical activity and cognitive performance?

BACKGROUND

There is a growing interest to understand the neurobiological mechanisms that drive the positive associations of physical activity and fitness with measures of cognitive performance. To better understand those mechanisms, several studies have employed eye-based measures (e.g., eye movement measures such as saccades, pupillary measures such as pupil dilation, and vascular measures such as retinal vessel diameter) deemed to be proxies for specific neurobiological mechanisms. However, there is currently no systematic review providing a comprehensive overview of these studies in the field of exercise-cognition science. Thus, this review aimed to address that gap in the literature.

METHODS

To identify eligible studies, we searched 5 electronic databases on October 23, 2022. Two researchers independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise (TESTEX scale, for interventional studies) and the critical appraisal tool from the Joanna Briggs Institute (for cross-sectional studies).

RESULTS

Our systematic review (n = 35 studies) offers the following main findings: (a) there is insufficient evidence available to draw solid conclusions concerning gaze-fixation-based measures; (b) the evidence that pupillometric measures, which are a proxy for the noradrenergic system, can explain the positive effect of acute exercise and cardiorespiratory fitness on cognitive performance is mixed; (c) physical training- or fitness-related changes of the cerebrovascular system (operationalized via changes in retinal vasculature) are, in general, positively associated with cognitive performance improvements; (d) acute and chronic physical exercises show a positive effect based on an oculomotor-based measure of executive function (operationalized via antisaccade tasks); and (e) the positive association between cardiorespiratory fitness and cognitive performance is partly mediated by the dopaminergic system (operationalized via spontaneous eye-blink rate).

CONCLUSION

This systematic review offers confirmation that eye-based measures can provide valuable insight into the neurobiological mechanisms that may drive positive associations between physical activity and fitness and measures of cognitive performance. However, due to the limited number of studies utilizing specific methods for obtaining eye-based measures (e.g., pupillometry, retinal vessel analysis, spontaneous eye blink rate) or investigating a possible dose-response relationship, further research is necessary before more nuanced conclusions can be drawn. Given that eye-based measures are economical and non-invasive, we hope this review will foster the future application of eye-based measures in the field of exercise-cognition science.

Assessing the Sensitivity of OCT-A Retinal Vasculature Metrics

Purpose

The purpose of this study was to examine the sensitivity of quantitative metrics of the retinal vasculature derived from optical coherence tomography angiography (OCT-A) images.

Methods

Full retinal vascular slab OCT-A images were obtained from 94 healthy participants. Capillary loss, at 1% increments up to 50%, was simulated by randomly removing capillary segments (1000 iterations of randomized loss for each participant at each percent loss). Thirteen quantitative metrics were calculated for each image: foveal avascular zone (FAZ) area, vessel density, vessel complexity index (VCI), vessel perimeter index (VPI), fractal dimension (FD), and parafoveal intercapillary area (PICA) measurements with and without the FAZ (mean PICA, summed PICA, PICA regularity, and PICA standard deviation [PICA SD]). The sensitivity of each metric was calculated as the percent loss at which 80% of the iterations for a participant fell outside of two standard deviations from the sample's normative mean.

Results

The most used OCT-A metrics, FAZ area and vessel density, were not significantly different from normative values until 27.69% and 16.00% capillary loss, respectively. Across the remaining metrics, metric sensitivity ranged from 6.37% (PICA SD without FAZ) to 39.78% (Summed PICA without FAZ).

Conclusions

The sensitivity of vasculature metrics for detecting random capillary loss varies substantially. Further efforts simulating different patterns of capillary loss are needed for comparison. Additionally, mapping the repeatability of metrics over time in a normal population is needed to further define metric sensitivity.

Translational Relevance

Quantitative metrics vary in their ability to detect vascular abnormalities in OCT-A images. Metric choice in screening studies will need to balance expected capillary abnormalities and the quality of the OCT-A images being used.

Review and comparison of retinal vessel calibre and geometry software and their application to diabetes, cardiovascular disease, and dementia

Developments in retinal imaging technologies have enabled the quantitative evaluation of the retinal vasculature. Changes in retinal calibre and/or geometry have been reported in systemic vascular diseases, including diabetes mellitus (DM), cardiovascular disease (CVD), and more recently in neurodegenerative diseases, such as dementia. Several retinal vessel analysis softwares exist, some being disease-specific, others for a broader context. In the research setting, retinal vasculature analysis using semi-automated software has identified associations between retinal vessel calibre and geometry and the presence of or risk of DM and its chronic complications, and of CVD and dementia, including in the general population. In this article, we review and compare the most widely used semi-automated retinal vessel analysis softwares and their associations with ocular imaging findings in common systemic diseases, including DM and its chronic complications, CVD, and dementia. We also provide original data comparing retinal calibre grading in people with Type 1 DM using two softwares, with good concordance.

Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated artificial intelligence measurements

Introduction: The purpose of this study is to assess the relationship between retinal vascular characteristics and cognitive function using artificial intelligence techniques to obtain fully automated quantitative measurements of retinal vascular morphological parameters. Methods: A deep learning-based semantic segmentation network ResNet101-UNet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters on fundus photographs. Retinal photographs centered on the optic disc of 3107 participants (aged 50-93 years) from the Beijing Eye Study 2011, a population-based cross-sectional study, were analyzed. The main parameters included the retinal vascular branching angle, vascular fractal dimension, vascular diameter, vascular tortuosity, and vascular density. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Results: The results showed that the mean MMSE score was 26.34 ± 3.64 (median: 27; range: 2-30). Among the participants, 414 (13.3%) were classified as having cognitive impairment (MMSE score < 24), 296 (9.5%) were classified as mild cognitive impairment (MMSE: 19-23), 98 (3.2%) were classified as moderate cognitive impairment (MMSE: 10-18), and 20 (0.6%) were classified as severe cognitive impairment (MMSE < 10). Compared with the normal cognitive function group, the retinal venular average diameter was significantly larger (p = 0.013), and the retinal vascular fractal dimension and vascular density were significantly smaller (both p < 0.001) in the mild cognitive impairment group. The retinal arteriole-to-venular ratio (p = 0.003) and vascular fractal dimension (p = 0.033) were significantly decreased in the severe cognitive impairment group compared to the mild cognitive impairment group. In the multivariate analysis, better cognition (i.e., higher MMSE score) was significantly associated with higher retinal vascular fractal dimension (b = 0.134, p = 0.043) and higher retinal vascular density (b = 0.152, p = 0.023) after adjustment for age, best corrected visual acuity (BCVA) (logMAR) and education level. Discussion: In conclusion, our findings derived from an artificial intelligence-based fully automated retinal vascular parameter measurement method showed that several retinal vascular morphological parameters were correlated with cognitive impairment. The decrease in retinal vascular fractal dimension and decreased vascular density may serve as candidate biomarkers for early identification of cognitive impairment. The observed reduction in the retinal arteriole-to-venular ratio occurs in the late stages of cognitive impairment.

Intrasession Repeatability of OCT Angiography Parameters in Neurodegenerative Disease

Purpose

To assess the intrasession repeatability of macular OCT angiography (OCTA) parameters in Alzheimer's disease (AD), mild cognitive impairment (MCI), Parkinson's disease (PD), and normal cognition (NC).

Design

Cross sectional study.

Subjects

Patients with a clinical diagnosis of AD, PD, MCI, or NC were imaged. Images with poor quality and of those with diabetes mellitus, glaucoma, or vitreoretinal disease were excluded from analysis.

Methods Intervention or Testing

All participants were imaged using the Zeiss Cirrus HD-5000 with AngioPlex (Carl Zeiss Meditec, Software Version 11.0.0.29946) and repeat OCTA images were obtained for both eyes. Perfusion density (PFD), vessel density (VD), and Foveal avascular zone (FAZ) area were measured from 3 × 3 mm and 6 × 6 mm OCTA images centered on the fovea using an ETDRS grid overlay.

Main Outcome Measures

Intraclass correlation coefficients were used to quantify repeatability of PFD, VD, and FAZ area measurements obtained from imaging.

Results

3 × 3 mm scans of 22 AD, 40 MCI, 21 PD, and 26 NC participants and 6 × 6 mm scans of 29 AD, 44 MCI, 29 PD, and 30 NC participants were analyzed. Repeatability values ranged from 0.64 (0.49-0.82) for 6 × 6 mm PFD in AD participants to 0.87 (0.67-0.92) for 3 × 3 mm PFD in AD participants. No significant differences were observed in repeatability between NC participants and those with neurodegenerative disease.

Conclusions

Overall, similar OCTA repeatability was observed between NC participants and those with neurodegeneration. Regardless of diagnostic group, macular OCTA metrics demonstrated moderate to good repeatability.

Financial Disclosures

The authors have no proprietary or commercial interest in any materials discussed in this article.

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.

Associations of Microvascular Dysfunction with Mild Cognitive Impairment and Cognitive Function Among Rural-Dwelling Older Adults in China

BACKGROUND

Microvascular dysfunction (MVD) may contribute to cognitive impairment and Alzheimer's disease, but evidence is limited.

OBJECTIVE

To investigate the association of composite and organ-specific MVD burden with mild cognitive impairment (MCI) and cognition among rural-dwelling Chinese older adults.

METHODS

In this population-based cross-sectional study, we assessed MVD makers using optical coherence tomographic angiography for retinal microvasculature features, brain magnetic resonance imaging scans for cerebral small vessel disease (CSVD), and serum biomarkers for MVD. A composite MVD score was generated from the aforementioned organ-specific parameters. We used a neuropsychological test battery to assess memory, verbal fluency, attention, executive function, and global cognitive function. MCI, amnestic MCI (aMCI), and non-amnestic MCI (naMCI) were diagnosed following the Petersen's criteria. Data was analyzed with the linear and logistic regression models.

RESULTS

Of the 274 dementia-free participants (age≥65 years), 56 were diagnosed with MCI, including 47 with aMCI and 9 with naMCI. A composite MVD score was statistically significantly associated with an odds ratio (OR) of 2.70 (95% confidence interval 1.12-6.53) for MCI and β-coefficient of -0.29 (-0.48--0.10) for global cognitive score after adjustment for socio-demographics, lifestyle factors, APOE genotype, the Geriatric Depression Scale score, serum inflammatory biomarkers, and cardiovascular comorbidity. A composite score of retinal microvascular morphology was associated with a multivariable-adjusted OR of 1.72 (1.09-2.73) for MCI and multivariable-adjusted β-coefficient of -0.11 (-0.22--0.01) for global cognitive score. A composite CSVD score was associated with a lower global cognitive score (β= -0.10; -0.17--0.02).

CONCLUSION

Microvascular dysfunction, especially in the brain and retina, is associated with MCI and poor cognitive function among rural-dwelling older adults.

Altered functional connectivity between the default mode network in diabetic retinopathy patients

OBJECTIVES

Previous studies have demonstrated that diabetic retinopathy is associated with cognitive impairment. This study aimed to investigate the intrinsic functional connectivity pattern within the default mode network (DMN) and its associations with cognitive impairment in diabetic retinopathy patients using resting-state functional MRI (rs-fMRI).

METHODS

A total of 34 diabetic retinopathy patients and 37 healthy controls were recruited for rs-fMRI scanning. Both groups were age, gender, and education level matched. The posterior cingulate cortex (PCC) was chosen as the region of interest for detecting functional connectivity changes.

RESULTS

Compared with the healthy control group, diabetic retinopathy patients showed increased functional connectivity between PCC and left medial superior frontal gyrus and increased functional connectivity between PCC and right precuneus.

CONCLUSION

Our study highlights that diabetic retinopathy patients show enhanced functional connectivity within DMN, suggesting that a compensatory increase of neural activity might occur in DMN, which offers new insight into the potential neural mechanism of cognitive impairment in diabetic retinopathy patients.

Retinal vessel geometry in patients with idiopathic epiretinal membrane

We investigated the associations between retinal vascular geometric measurements and idiopathic epiretinal membrane (ERM). Whether changes in retinal vascular geometry are independent of systemic cardiovascular risk factors was also evaluated. This retrospective, cross sectional study included 98 patients with idiopathic ERM, and 99 healthy age-matched controls. Quantitative retinal vascular parameters were measured from digital retinal fundus photographs using a semi-automated computer-assisted program. Multivariate logistic regression analyses were performed to evaluate associations between retinal vascular geometric parameters and the presence of idiopathic ERM after adjusting for systemic cardiovascular risk factors. There was no significant difference in the baseline characteristics of the two groups, except that the ERM group had a higher proportion of females than the control group. In multivariate regression analyses, female sex (odds ratio [OR] 0.402; 95% CI 0.196-0.802; P = 0.011), wider retinal venular caliber (OR 16.852; 95% CI 5.384-58.997; P < 0.001) and decreased total fractal dimension (OR 0.156; 95% CI 0.052-0.440; P = 0.001) were associated with idiopathic ERM. Idiopathic ERM was associated with alterations in global retinal microvascular geometric parameters, wider retinal venules, and less complex vascular branching patterns, independent of cardiovascular risk factors.

Association of Central Retinal Arteriolar and Venular Equivalents with Brain-aging and Macular Ganglion Cell-inner Plexiform Layer Thickness

INTRODUCTION

Neurodegeneration and cognitive decline in aging are growing public health concerns. This study investigates associations between central retinal arteriolar and venular equivalents (CRAE, CRVE) and brain-aging, a sensory and cognitive test composite measure, and macular ganglion cell-inner plexiform layer (mGCIPL) thickness, a biomarker of neurodegeneration.

METHODS

Beaver Dam Offspring Study (BOSS) participants are adult children (baseline (2005-2008) age 21-84 years) of the population-based Epidemiology of Hearing Loss Study participants. Follow-up occurred every 5 years. In 2010-2013, fundus photographs were used to measure retinal vessels. A brain-aging score was constructed by principal component analysis using sensorineural and cognitive data. Associations between incident brain-aging and vessel measures were investigated using logistic regression. Associations between CRAE and CRVE and mGCIPL thickness, measured in 2015-2017, were also investigated.

RESULTS

Participants (N = 2381; mean age: 53.9 years (SD = 9.8); 54% women) had a mean CRAE and CRVE of 148.8 µm (SD = 14.5) and 221.7 µm (SD = 20.7), respectively. Among those without ocular conditions, wider CRAE was associated with decreased 5-year brain-aging risk (33% per SD CRAE increase). Both vessel measures were independently associated with mGCIPL thickness. The mGCIPL thickness increased by approximately 1.7 µm and 2.0 µm per SD increase in CRAE and CRVE, respectively.

DISCUSSION

The association of CRAE with incident brain-aging indicates its potential use as a screening tool among those without eye disease. The associations between CRAE and CRVE and mGCIPL thickness indicate narrower vasculature could affect neuronal health. These associations point to potential usefulness of retinal vessel measurements to identify people at higher risk of sensorineural declines and neurodegeneration.

Retinal Vascular Changes in Alzheimer's Dementia and Mild Cognitive Impairment: A Pilot Study Using Ultra-Widefield Imaging

Purpose

Retinal microvascular abnormalities measured on retinal images are a potential source of prognostic biomarkers of vascular changes in the neurodegenerating brain. We assessed the presence of these abnormalities in Alzheimer's dementia and mild cognitive impairment (MCI) using ultra-widefield (UWF) retinal imaging.

Methods

UWF images from 103 participants (28 with Alzheimer's dementia, 30 with MCI, and 45 with normal cognition) underwent analysis to quantify measures of retinal vascular branching complexity, width, and tortuosity.

Results

Participants with Alzheimer's dementia displayed increased vessel branching in the midperipheral retina and increased arteriolar thinning. Participants with MCI displayed increased rates of arteriolar and venular thinning and a trend for decreased vessel branching.

Conclusions

Statistically significant differences in the retinal vasculature in peripheral regions of the retina were observed among the distinct cognitive stages. However, larger studies are required to establish the clinical importance of our findings. UWF imaging may be a promising modality to assess a larger view of the retinal vasculature to uncover retinal changes in Alzheimer's disease.

Translational Relevance

This pilot work reports an investigation into which retinal vasculature measurements may be useful surrogate measures of cognitive decline, as well as technical developments (e.g., measurement standardization), that are first required to establish their recommended use and translational potential.

Macular vessel density in the superficial plexus is not associated to cerebrospinal fluid core biomarkers for Alzheimer's disease in individuals with mild cognitive impairment: The NORFACE cohort

Background

Optical coherence tomography angiography (OCT-A) is a novel method in the dementia field that allows the detection of retinal vascular changes. The comparison of OCT-A measures with established Alzheimer's disease (AD)-related biomarkers is essential to validate the former as a marker of cerebrovascular impairment in the AD continuum. We aimed to investigate the association of macular vessel density (VD) in the superficial plexus quantified by OCT-A with the AT(N) classification based on cerebrospinal fluid (CSF) Aβ1-42, p181-tau and t-tau measurements in individuals with mild cognitive impairment (MCI).

Materials and methods

Clinical, demographic, ophthalmological, OCT-A and CSF core biomarkers for AD data from the Neuro-ophthalmology Research at Fundació ACE (NORFACE) project were analyzed. Differences in macular VD in four quadrants (superior, nasal, inferior, and temporal) among three AT(N) groups [Normal, Alzheimer and Suspected non-Alzheimer pathology (SNAP)] were assessed in a multivariate regression model, adjusted for age, APOE ε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, chronic obstructive pulmonary disease and smoking habit, using the Normal AT(N) group as the reference category.

Results

The study cohort comprised 144 MCI participants: 66 Normal AT(N), 45 Alzheimer AT(N) and 33 SNAP AT(N). Regression analysis showed no significant association of the AT(N) groups with any of the regional macular VD measures (all, p > 0.16). The interaction between sex and AT(N) groups had no effect on differentiating VD. Lastly, CSF Aβ1-42, p181-tau and t-tau measures were not correlated to VD (all r < 0.13; p > 0.13).

Discussion

Our study showed that macular VD measures were not associated with the AT(N) classification based on CSF biomarkers in patients with MCI, and did not differ between AD and other underlying causes of cognitive decline in our cohort.

Deep Reinforcement Learning-Based Retinal Imaging in Alzheimer's Disease: Potential and Perspectives

Alzheimer's disease (AD) remains a global health challenge in the 21st century due to its increasing prevalence as the major cause of dementia. State-of-the-art artificial intelligence (AI)-based tests could potentially improve population-based strategies to detect and manage AD. Current retinal imaging demonstrates immense potential as a non-invasive screening measure for AD, by studying qualitative and quantitative changes in the neuronal and vascular structures of the retina that are often associated with degenerative changes in the brain. On the other hand, the tremendous success of AI, especially deep learning, in recent years has encouraged its incorporation with retinal imaging for predicting systemic diseases. Further development in deep reinforcement learning (DRL), defined as a subfield of machine learning that combines deep learning and reinforcement learning, also prompts the question of how it can work hand in hand with retinal imaging as a viable tool for automated prediction of AD. This review aims to discuss potential applications of DRL in using retinal imaging to study AD, and their synergistic application to unlock other possibilities, such as AD detection and prediction of AD progression. Challenges and future directions, such as the use of inverse DRL in defining reward function, lack of standardization in retinal imaging, and data availability, will also be addressed to bridge gaps for its transition into clinical use.

Retinal parameters, cortical cerebral microinfarcts, and their interaction with cognitive impairment

BACKGROUND

Quantitative changes in retinal vessels and thinning of optic nerves have been associated with subclinical (atherosclerosis, inflammation) and clinical age-related brain pathologies (stroke and neurodegeneration). However, data on the association between both retinal vascular and neuronal parameters with cortical cerebral microinfarcts (CMIs) and how these factors jointly influence cognition are lacking.

AIM

We investigated the association of retinal vascular and neuronal changes with CMIs on 3 T MRI and explored their interaction with cognitive impairment in a memory-clinic population.

METHODS

A total of 538 participants were included. Retinal vascular parameters (caliber, tortuosity, and fractal dimension) were measured from retinal fundus photographs using a semi-automated computer-assisted program. Retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL) thicknesses were obtained from optical coherence tomography. Cortical CMIs were defined as hypointense on T1-weighted MRI, <5 mm in diameter and restricted to the cortex. Cognition was assessed using Clinical Dementia Rating Sum-of-Boxes (CDR-SoB) score and detailed neuropsychological test. Multivariable regression analysis was conducted adjusting for age, sex, hypertension, hyperlipidemia, diabetes mellitus, smoking, diagnosis, white matter hyperintensities volume, lacunes, and cerebral microbleeds.

RESULTS

Larger venular caliber (Rate ratios (RR): 1.15, 95% CI: 1.01-1.38, p = 0.014), increased venular fractal dimension (RR: 1.58, 95% CI: 1.31-1.91, p ⩽ 0.001), increased venular tortuosity (RR: 1.54, 95% CI: 1.35-1.75, p ⩽ 0.001), and thinner GC-IPL (RR: 1.24, 95% CI: 1.13-1.36, p ⩽ 0.001) were associated with CMI counts. Among individuals in highest tertile of retinal parameters, a significant interaction was observed between venular tortuosity (RR: 1.12, 95% CI: 1.02-1.22, p-interaction = 0.014) and GC-IPL (RR: 1.05, 95% CI: 1.01-1.11, p-interaction < 0.001) with CMIs on CDR-SoB.

CONCLUSION

Retinal vascular and neuronal parameters are associated with cortical CMIs, and persons with both pathologies are likely to have cognitive impairment. Further studies may be warranted to evaluate the clinical utility of retinal parameters and CMI in risk prediction for cognitive dysfunction.