Ocular biomarkers and their role in the early diagnosis of neurocognitive disorders

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.

Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening

BACKGROUND/AIMS

To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of the multimodal ophthalmic imaging and artificial neural networks.

METHODS

Patients with cognitive impairment and cognitively healthy individuals were recruited. All subjects underwent medical history, blood pressure measurement, the Montreal Cognitive Assessment, medical optometry, intraocular pressure and custom-built multimodal ophthalmic imaging, which integrated pupillary light reaction, multispectral imaging, laser speckle contrast imaging and retinal oximetry. Multidimensional parameters were analysed by Student's t-test. Logistic regression analysis and back-propagation neural network (BPNN) were used to identify the predictive capability for cognitive impairment.

RESULTS

This study included 104 cognitive impairment patients (61.5% female; mean (SD) age, 68.3 (9.4) years), and 94 cognitively healthy age-matched and sex-matched subjects (56.4% female; mean (SD) age, 65.9 (7.6) years). The variation of most parameters including decreased pupil constriction amplitude (CA), relative CA, average constriction velocity, venous diameter, venous blood flow and increased centred retinal reflectance in 548 nm (RC548) in cognitive impairment was consistent with previous studies while the reduced flow acceleration index and oxygen metabolism were reported for the first time. Compared with the logistic regression model, BPNN had better predictive performance (accuracy: 0.91 vs 0.69; sensitivity: 93.3% vs 61.70%; specificity: 90.0% vs 68.66%).

CONCLUSIONS

This study demonstrates retinal spectral signature alteration, neurodegeneration and angiopathy occur concurrently in cognitive impairment. The combination of multimodal ophthalmic imaging and BPNN can be a useful tool for predicting cognitive impairment with high performance for community screening.

Age-related macular degeneration and neurodegenerative disorders: Shared pathways in complex interactions

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness in the elderly, and neurodegenerative disorders such as Alzheimer disease and Parkinson disease are debilitating conditions that affect millions worldwide. Despite the different clinical manifestations of these diseases, growing evidence suggests that they share common pathways in their pathogenesis including inflammation, oxidative stress, and impaired autophagy. In this review, we explore the complex interactions between AMD and neurodegenerative disorders, focusing on their shared mechanisms and potential therapeutic targets. We also discuss the current opportunities and challenges for developing effective treatments that can target these pathways to prevent or slow down disease progression in AMD. Some of the promising strategies that we explore include modulating the immune response, reducing oxidative stress, enhancing autophagy and lysosomal function, and targeting specific protein aggregates or pathways. Ultimately, a better understanding of the shared pathways between AMD and neurodegenerative disorders may pave the way for novel and more efficacious treatments.

Retina Oculomics in Neurodegenerative Disease

Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging technologies including electroretinography, optical coherence tomography (OCT), confocal scanning laser ophthalmoscopy, fluorescence lifetime imaging ophthalmoscopy, and OCT angiography have revolutionized the ability to understand systemic diseases and even detect them earlier than clinical manifestations for earlier intervention. With the advent of increasingly large ophthalmic imaging datasets, machine learning models can be integrated into these ocular imaging biomarkers to provide further insights and prognostic predictions of neurodegenerative disease. In this manuscript, we review the use of ophthalmic imaging to provide insights into neurodegenerative diseases including Alzheimer Disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, and Huntington Disease. We discuss recent advances in ophthalmic technology including eye-tracking technology and integration of artificial intelligence techniques to further provide insights into these neurodegenerative diseases. Ultimately, oculomics opens the opportunity to detect and monitor systemic diseases at a higher acuity. Thus, earlier detection of systemic diseases may allow for timely intervention for improving the quality of life in patients with neurodegenerative disease.

Detecting Structural Changes in the Choroidal Layer of the Eye in Neurodegenerative Disease Patients through Optical Coherence Tomography Image Processing

PURPOSE

To evaluate alterations of the choroid in patients with a neurodegenerative disease versus healthy controls, a custom algorithm based on superpixel segmentation was used.

DESIGN

A cross-sectional study was conducted on data obtained in a previous cohort study.

SUBJECTS

Swept-source optical coherence tomography (OCT) B-scan images obtained using a Triton (Topcon, Japan) device were compiled according to current OSCAR IB and APOSTEL OCT image quality criteria. Images were included from three cohorts: multiple sclerosis (MS) patients, Parkinson disease (PD) patients, and healthy subjects. Only patients with early-stage MS and PD were included.

METHODS

In total, 104 OCT B-scan images were processed using a custom superpixel segmentation (SpS) algorithm to detect boundary limits in the choroidal layer and the optical properties of the image. The algorithm groups pixels with similar structural properties to generate clusters with similar meaningful properties.

MAIN OUTCOMES

SpS selects and groups the superpixels in a segmented choroidal area, computing the choroidal optical image density (COID), measured as the standard mean gray level, and the total choroidal area (CA), measured as px2.

RESULTS

The CA and choroidal density (CD) were significantly reduced in the two neurodegenerative disease groups (higher in PD than in MS) versus the healthy subjects (p < 0.001); choroidal area was also significantly reduced in the MS group versus the healthy subjects. The COID increased significantly in the PD patients versus the MS patients and in the MS patients versus the healthy controls (p < 0.001).

CONCLUSIONS

The SpS algorithm detected choroidal tissue boundary limits and differences optical density in MS and PD patients versus healthy controls. The application of the SpS algorithm to OCT images potentially acts as a non-invasive biomarker for the early diagnosis of MS and PD.

Retinal Vessel Density and Retinal Nerve Fiber Layer Thickness: A Prospective Study of One-Year Follow-Up of Patients with Parkinson's Disease

Objective

This study aims to compare the superficial vascular density from the macular region and the retinal nerve fiber layer (RNFL) thickness from the optic disc region between Parkinson's disease (PD) patients and controls.

Methods

We enrolled 56 idiopathic PD patients, totaling 86 eyes (PD group), and 45 sex- and age-matched healthy individuals, amounting to 90 eyes (control group). All subjects underwent examination using Zeiss wide-field vascular optical coherence tomography (OCT) (Cirrus HD-OCT 5000 Carl Zeiss, Germany), with a scanning range of 3 mm × 3 mm. We divided the images into two concentric circles with diameters of 1 mm and 3 mm at the macular fovea's center. Patients with PD were evaluated during their "off" phase using the Unified Parkinson's Disease Rating Scale III (UPDRS-III) and the Hoehn-Yahr scale (H-Y scale) to assess disease severity.

Results

The PD group exhibited significantly lower RNFL thickness (106.13±12.36 μm) compared to the control group (115.95±11.37 μm, P < 0.05). Similarly, the superficial retinal vessel length density was significantly lower in the PD group (20.7 [19.62, 22.17] mm-1) than in the control group (21.79±1.16 mm-1, P < 0.05). Correlation analysis revealed a negative correlation between RNFL thickness and UPDRS III score (rs=-0.036, P=0.037), and RNFL thickness tended to decrease with increasing severity of movement disorders. However, during the 6 and 12-month follow-up of some PD patients, we observed no progressive thinning of the RNFL or decreased superficial vascular density.

Conclusion

PD patients show retinal structural damage characterized by RNFL thinning and reduced retinal vessel length density. However, RNFL thickness did not correlate with vascular density nor did it decrease with the disease's progression.

Age-associated changes in electroretinography measures in companion dogs

PURPOSE

To determine the association between age and retinal full-field electroretinographic (ERG) measures in companion (pet) dogs, an important translational model species for human neurologic aging.

METHODS

Healthy adult dogs with no significant ophthalmic abnormalities were included. Unilateral full-field light- and dark-adapted electroretinography was performed using a handheld device, with mydriasis and topical anaesthesia. Partial least squares effect screening analysis was performed to determine the effect of age, sex, body weight and use of anxiolytic medication on log-transformed ERG peak times and amplitudes; age and anxiolytic usage had significant effects on multiple ERG outcomes. Mixed model analysis was performed on data from dogs not receiving anxiolytic medications.

RESULTS

In dogs not receiving anxiolytics, median age was 118 months (interquartile range 72-140 months, n = 77, 44 purebred, 33 mixed breed dogs). Age was significantly associated with prolonged peak times of a-waves (dark-adapted 3 and 10 cds/m2 flash p < 0.0001) and b-waves (cone flicker p = 0.03, dark-adapted 0.01 cds/m2 flash p = 0.001). Age was also significantly associated with reduced amplitudes of a-waves (dark-adapted 3 cds/m2 flash p < 0.0001, 10 cds/m2 flash p = 0.005) and b-waves (light-adapted 3 cds/m2 flash p < 0.0001, dark-adapted 0.01 cds/m2 flash p = 0.0004, 3 cds/m2 flash p < 0.0001, 10 cds/m2 flash p = 0.007) and flicker (light-adapted 30 Hz 3 cds/m2 p = 0.0004). Within the Golden Retriever breed, these trends were matched in a cross-sectional analysis of 6 individuals that received no anxiolytic medication.

CONCLUSIONS

Aged companion dogs have slower and reduced amplitude responses in both rod- and cone-mediated ERG. Consideration of anxiolytic medication use should be made when conducting ERG studies in dogs.

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.

Artificial intelligence in dementia

PURPOSE OF REVIEW

Artificial intelligence tools are being rapidly integrated into clinical environments and may soon be incorporated into dementia diagnostic paradigms. A comprehensive review of emerging trends will allow physicians and other healthcare providers to better anticipate and understand these powerful tools.

RECENT FINDINGS

Machine learning models that utilize cerebral biomarkers are demonstrably effective for dementia identification and prediction; however, cerebral biomarkers are relatively expensive and not widely available. As eye images harbor several ophthalmic biomarkers that mirror the state of the brain and can be clinically observed with routine imaging, eye-based machine learning models are an emerging area, with efficacy comparable with cerebral-based machine learning models. Emerging machine learning architectures like recurrent, convolutional, and partially pretrained neural networks have proven to be promising frontiers for feature extraction and classification with ocular biomarkers.

SUMMARY

Machine learning models that can accurately distinguish those with symptomatic Alzheimer's dementia from those with mild cognitive impairment and normal cognition as well as predict progressive disease using relatively inexpensive and accessible ocular imaging inputs are impactful tools for the diagnosis and risk stratification of Alzheimer's dementia continuum. If these machine learning models can be incorporated into clinical care, they may simplify diagnostic efforts. Recent advancements in ocular-based machine learning efforts are promising steps forward.

Macular microcirculation characteristics in Parkinson's disease evaluated by OCT-Angiography: a literature review

PURPOSE

Given the fact that retina may provide a window into the central nervous system, there has been interest in identifying retinal biomarkers as predicting factors of pathological processes in neurodegenerative disorders. Emerging evidence has suggested that macular microcirculation changes in Parkinson disease (PD) may indicate the alterations of cerebral microvasculature. The use of Optical Coherence Tomography Angiography (OCT-A) has attracted significant attention in recent years as this technique offers a detailed analysis of the existence of changes at the macular capillary plexus.

METHODS

A detailed review of the literature was performed in PubMed until June 2021. We identified all papers referring to the alterations of the macular capillary plexus in PD using OCT-A.

RESULTS

A comprehensive update indicates that microvasculature alterations of the macular capillary plexus utilizing OCT-A may comprise useful biomarkers regarding the cerebral vasculature in PD. Since the available evidence is limited, additional studies are warranted to establish the OCT-A parameters as predicting factors in clinical practice.

CONCLUSIONS

A review of the existing literature sheds light on the microvasculature changes of the macular capillary plexus as seen on OCT-A in PD patients. The current article discusses notable aspects of key publications on the topic, highlights the importance of the potential long-term effectiveness of OCT-A biomarkers in PD and proposes the need for further future research.