Optical coherence tomography in mild cognitive impairment - Systematic review and meta-analysis

Retinal imaging with hand-held optical coherence tomography in older people with or without postoperative delirium after hip fracture surgery: A feasibility study

INTRODUCTION

Postoperative delirium in older people may result from the interaction between intrinsic brain vulnerability (e.g. neurodegeneration) and precipitating factors (e.g. surgery induced cytokines). Intrinsic brain vulnerability may be overt (e.g. Alzheimer's disease) or preclinical. In cognitively intact older people presenting for surgery, identification of preclinical neurodegeneration using bedside tools could aid postoperative delirium risk stratification. Thinning of the circumpapillary retinal nerve fibre layer thickness is associated with neurodegenerative disorders e.g. Alzheimer's disease. We propose that thinning of the retinal nerve fibre layer may be present some older people with postoperative delirium due to preclinical neurodegeneration, albeit to a lesser extent than in overt dementia.

OBJECTIVES

The primary objective: Feasibility of acquiring usable retinal images with the hand-held optical coherence device, at the bedside of older, hip fracture surgery patients. Secondary objective: Comparison of the circumpapillary retinal nerve fibre layer thickness between people who did/did not have postoperative delirium. Proportion of exclusions due to retinal pathology.

METHOD

Feasibility study involving 30, cognitively intact, older people recovering from hip fracture surgery. Retinal images were obtained using the hand-held optical coherence tomography device at the participants' bedside. Imaging was deferred in participants who had postoperative delirium.

RESULTS

Retinal images that could be assessed for circumpapillary retinal nerve fibre layer thickness were obtained in 26 participants (22 no postoperative delirium, 4 postoperative delirium). The mean circumpapillary retinal nerve fibre layer thickness was lower in the participants who had postoperative delirium compared to those who did not experience postoperative delirium (Mean (95% CI) of 76.50 (62.60-90.40) vs 89.19 (85.41-92.97) respectively).

CONCLUSION

Retinal imaging at the patient's bedside, using hand-held OCT is feasible. Our data suggests that the circumpapillary retinal nerve fibre layer may be thinner in older people who experience postoperative delirium compared to those who do not. Further studies are required.

Alzheimer's disease pathophysiology in the Retina

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

Retinal clues for selective neuronal loss in multiple sclerosis

OBJECTIVE

The relationship between the cell body layer and the dendritic network layer of the retina and cognitive performance (CP) in MS patients has not been examined separately. The objective of this study is to predict cognitive impairment (CI) in RRMS patients and to examine the relationship between CP and ganglion cell layer (GCL), inner plexiform layer (IPL), and GCL divided by IPL (GCL/IPL).

METHODS

Ophthalmological evaluation, retinal segmentation, and Symbol Digit Modalities Test (SDMT) were performed on 102 RRMS patients and 54 healthy subjects. The relationships of GCL, IPL, and GCL/IPL with CP in eyes without a history of optic neuritis were investigated using Spearman's correlation. Models were created by accepting 1 standard deviation less of the SDMT mean of the control group as the limit for CI. The cutoff value of the GCL/IPL variable that could predict CI was calculated by ROC analysis, and the ability to accurately predict CI was tested with binary logistic regression.

RESULTS

No correlation was found between OCT parameters and CP in healthy subjects. Correlation was found between GCL thickness and GCL/IPL variable and CP in RRMS patients (r=0.235, r=0.667 respectively). A GCL/IPL value of 1.255 was able to identify CI with 81.8% sensitivity and 75.9% specificity (AUC=0.844, LR=3.38) and predicted CI with 74.5% accuracy (Nagelkerke R2=0.439).

CONCLUSION

In RRMS patients, the IPL thickness is unrelated to CP. Therewithal, the GCL/IPL-CP relationship is stronger than the GCL-CP relationship and GCL/IPL can predict CI.

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.

Peering further into the mind's eye: combining visual evoked potential and optical coherence tomography measures enhances insight into the variance in cognitive functioning in multiple sclerosis

BACKGROUND

Spectral Optical Coherence Tomography (OCT) and Visual Evoked Potentials (VEPs) have both emerged as potentially useful biomarkers of cognitive decline in people with multiple sclerosis (PwMS). Their combined use may provide additional predictive value for identifying disease impact, progression, and remyelination capacity above-and-beyond what is captured using either approach alone.

OBJECTIVE

We examined the relationship between OCT/VEP measures and cognitive functioning in 205 PwMS. OCT measures included Retinal Nerve Fiber Layer Volume (RNFLV), Papillo-Macular Bundle Volume (PBMV), and Macular Volume (MV). VEP measures included latency of the P100, and inter-ocular latency. Cognitive performance was evaluated across seven separate domains of performance, and for overall cognition, using the NeuroTrax computerized testing battery.

RESULTS

Both OCT and VEP measures were significantly correlated with cognitive performance across several domains. Linear regression models that controlled for the influence of visual acuity revealed (1) that reduced MV was significantly predictive of poorer visual-spatial functioning, and (2) that delayed VEP latency was significantly predictive of performance in global cognitive functioning and visual-spatial functioning, after controlling for multiple comparisons. Among PwMS with normal visual acuity, PwMS with a combination of both relatively low MV and delayed VEP latency tended to have poorer performance in the domains of global, executive, and visual-spatial functioning compared to PwMS with both high MV and normal VEP latency.

CONCLUSION

Approaches that combine the use of OCT and VEP measures can enhance insight into underlying factors that contribute to variance in cognitive functioning in PwMS.

Literature Commentary

The format of Lit Commentary is a bit different beginning in this issue to save space in the Journal. We no longer start with a list of the papers reviewed, and we no longer print a copy of the abstracts, as the articles are easily available online. We welcome feedback about these changes. In this issue of JNO Drs. Mark L. Moster, Marc Dinkin, and Deborah I. Friedman discuss the following six articles.

The Healthy Brain Initiative (HBI): A prospective cohort study protocol

BACKGROUND

The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging.

METHODS

HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline.

ETHICS AND EXPECTED IMPACT

HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, creating comprehensive diagnostic evaluations, and providing the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.

Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.

The Healthy Brain Initiative (HBI): A prospective cohort study protocol

Background

The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging.

Methods

HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline.

Ethics and expected impact

HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, create comprehensive diagnostic evaluations, and provide the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.

Optical Coherence Tomography Assessment of Macular Thickness in Alzheimer's Dementia with Different Neuropsychological Severities

This retrospective case-control study aimed to investigate associations between disease severity of Alzheimer's dementia (AD) and macular thickness. Data of patients with AD who were under medication (n = 192) between 2013 and 2020, as well as an age- and sex-matched control group (n = 200) with normal cognitive function, were included. AD patients were divided into subgroups according to scores of the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). Macular thickness was analyzed via the Early Treatment Diabetic Retinopathy Study (ETDRS) grid map. AD patients had significant reductions in full macula layers, including inner circle, outer inferior area, and outer nasal area of the macula. Similar retinal thinning was noted in ganglion cells and inner plexiform layers. Advanced AD patients (MMSE score < 18 or CDR ≥ 1) showed more advanced reduction of macular thickness than the AD group (CDR = 0.5 or MMSE ≥ 18), indicating that severe cognitive impairment was associated with thinner macular thickness. Advanced AD is associated with significant macula thinning in full retina and inner plexiform layers, especially at the inner circle of the macula. Macular thickness may be a useful biomarker of AD disease severity. Retinal imaging may be a non-invasive, low-cost surrogate for AD.

Ocular Biomarkers for Alzheimer Disease Dementia: An Umbrella Review of Systematic Reviews and Meta-analyses

Importance

Several ocular biomarkers have been proposed for the early detection of Alzheimer disease (AD) and mild cognitive impairment (MCI), particularly fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA).

Objective

To perform an umbrella review of systematic reviews to assess the diagnostic accuracy of ocular biomarkers for early diagnosis of Alzheimer disease.

Data Sources

MEDLINE, Embase, and PsycINFO were searched from January 2000 to November 2021. The references of included reviews were also searched.

Study Selection

Systematic reviews investigating the diagnostic accuracy of ocular biomarkers to detect AD and MCI, in secondary care or memory clinics, against established clinical criteria or clinical judgment.

Data Extraction and Synthesis

The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline checklist was followed and the Risk Of Bias in Systematic reviews tool was used to assess review quality.

Main Outcomes and Measures

The prespecified outcome was the accuracy of ocular biomarkers for diagnosing AD and MCI. The area under the curve (AUC) was derived from standardized mean difference.

Results

From the 591 titles, 14 systematic reviews were included (median [range] number of studies in each review, 14 [5-126]). Only 4 reviews were at low risk of bias on all Risk of Bias in Systematic Reviews domains. The imaging-derived parameters with the most evidence for detecting AD compared with healthy controls were OCT peripapillary retinal nerve fiber layer thickness (38 studies including 1883 patients with AD and 2510 controls; AUC = 0.70; 95% CI, 0.53-0.79); OCTA foveal avascular zone (5 studies including 177 patients with AD and 371 controls; AUC = 0.73; 95% CI, 0.50-0.89); and saccadic eye movements prosaccade latency (30 studies including 651 patients with AD/MCI and 771 controls; AUC = 0.64; 95% CI, 0.58-0.69). Antisaccade error was investigated in fewer studies (12 studies including 424 patients with AD/MCI and 382 controls) and yielded the best accuracy (AUC = 0.79; 95% CI, 0.70-0.88).

Conclusions and Relevance

This umbrella review has highlighted limitations in design and reporting of the existing research on ocular biomarkers for diagnosing AD. Parameters with the best evidence showed poor to moderate diagnostic accuracy in cross-sectional studies. Future longitudinal studies should investigate whether changes in OCT and OCTA measurements over time can yield accurate predictions of AD onset.

Neurovascular coupling unit dysfunction and dementia: Retinal measurements as tools to move towards population-based evidence

Dysfunction of the neurovascular coupling unit may be an important contributor to dementia. The neurovascular coupling unit comprises neuronal structures (e.g. astrocytes) and vascular structures (e.g. endothelial cells) that functionally interact both at the level of the arterioles as well as at the capillary level (blood-brain barrier) to regulate optimal metabolic conditions in the brain. However, it remains unclear how and to what extent dysfunction of the neurovascular coupling unit contributes to the early-stage pathobiology of dementia. Currently, limited data are available on the association between neurovascular coupling unit dysfunction, as quantified by cerebral imaging techniques, and cognitive performance. In particular, there is a lack of population-based human data (defined as studies with a sample size ~n>500). This is an important limitation because population-based studies, in comparison with smaller clinical studies, provide data which is better representative of the general population; are less susceptible to selection bias; and have a larger statistical power to detect small associations. To acquire population-based data, however, alternative imaging techniques than cerebral imaging techniques may be required. Disadvantages of cerebral imaging techniques, which limit use in population-based studies, are that these techniques are relatively expensive, time-consuming, and/or invasive. In this review, we propose that retinal imaging techniques can be used for population-based studies: on the one hand the retina and brain have many anatomical and physiological similarities; and on the other hand retinal imaging techniques are non-invasive, highly accurate, relatively inexpensive, and require relatively short measurement time. To provide support for this concept, we provide an overview on the human (population-based) evidence on the associations of retinal indices of neurodegeneration, microvascular dysfunction, and dysfunction of the neurovascular coupling unit with magnetic resonance imaging (MRI) features of structural brain abnormalities and cognitive performance.

Using Optical Coherence Tomography to Screen for Cognitive Impairment and Dementia

BACKGROUND

Screening for Alzheimer's disease and related disorders (ADRD) and mild cognitive impairment (MCI) could increase case identification, enhance clinical trial enrollment, and enable early intervention. MCI and ADRD screening would be most beneficial if detection measures reflect neurodegenerative changes. Optical coherence tomography (OCT) could be a marker of neurodegeneration (part of the amyloid-tau-neurodegeneration (ATN) framework).

OBJECTIVE

To determine whether OCT measurements can be used as a screening measure to detect individuals with MCI and ADRD.

METHODS

A retrospective cross-sectional study was performed on 136 participants with comprehensive clinical, cognitive, functional, and behavioral evaluations including OCT with a subset (n = 76) completing volumetric MRI. Pearson correlation coefficients tested strength of association between OCT and outcome measures. Receiver operator characteristic curves assessed the ability of OCT, patient-reported outcomes, and cognitive performance measures to discriminate between individuals with and without cognitive impairment.

RESULTS

After controlling for age, of the 6 OCT measurements collected, granular cell layer-inner plexiform layer (GCL + IPL) thickness best correlated with memory, global cognitive performance, Clinical Dementia Rating, and hippocampal atrophy. GCL + IPL thickness provided good discrimination in cognitive status with a cut-off score of 75μm. Combining GCL + IPL thickness as a proxy marker for hippocampal atrophy with a brief patient-reported outcome and performance measure correctly classified 87%of MCI and ADRD participants.

CONCLUSION

Multimodal approaches may improve recognition of MCI and ADRD. OCT has the potential to be a practical, non-invasive biomarker for ADRD providing a screening platform to quickly identify at-risk individuals for further clinical evaluation or research enrollment.