Ocular fundus reference images from optical coherence tomography

When Sex Matters: Differences in the Central Nervous System as Imaged by OCT through the Retina

BACKGROUND

Retinal texture has gained momentum as a source of biomarkers of neurodegeneration, as it is sensitive to subtle differences in the central nervous system from texture analysis of the neuroretina. Sex differences in the retina structure, as detected by layer thickness measurements from optical coherence tomography (OCT) data, have been discussed in the literature. However, the effect of sex on retinal interocular differences in healthy adults has been overlooked and remains largely unreported.

METHODS

We computed mean value fundus images for the neuroretina layers as imaged by OCT of healthy individuals. Texture metrics were obtained from these images to assess whether women and men have the same retina texture characteristics in both eyes. Texture features were tested for group mean differences between the right and left eye.

RESULTS

Corrected texture differences exist only in the female group.

CONCLUSIONS

This work illustrates that the differences between the right and left eyes manifest differently in females and males. This further supports the need for tight control and minute analysis in studies where interocular asymmetry may be used as a disease biomarker, and the potential of texture analysis applied to OCT imaging to spot differences in the retina.

Retinal imaging in animal models: Searching for biomarkers of neurodegeneration

There is a pressing need for novel diagnostic and progression biomarkers of neurodegeneration. However, the inability to determine disease duration and stage in patients with Alzheimer's disease (AD) hinders their discovery. Because animal models of disease allow us to circumvent some of these limitations, they have proven to be of paramount importance in clinical research. Due to the clear optics of the eye, the retina combined with optical coherence tomography (OCT) offers the perfect opportunity to image neurodegeneration in the retina in vivo, non-invasively, directly, quickly, and inexpensively. Based on these premises, our group has worked towards uncovering neurodegeneration-associated changes in the retina of the triple-transgenic mouse model of familial AD (3×Tg-AD). In this work, we present an overview of our work on this topic. We report on thickness variations of the retina and retinal layers/layer aggregates caused by healthy aging and AD-like conditions and discuss the implications of focusing research efforts solely on retinal thickness. We explore what other information is embedded in the OCT data, extracted based on texture analysis and deep-learning approaches, to further identify biomarkers that could be used for early detection and diagnosis. We were able to detect changes in the retina of the animal model of AD as early as 1 month of age. We also discuss our work to develop an optical coherence elastography system to measure retinal elasticity, which can be used in conjunction with conventional OCT. Finally, we discuss the potential application of these technologies in human patients and the steps needed to make OCT a helpful screening tool for the detection of neurodegeneration.

Stage-independent biomarkers for Alzheimer's disease from the living retina: an animal study

The early diagnosis of neurodegenerative disorders is still an open issue despite the many efforts to address this problem. In particular, Alzheimer’s disease (AD) remains undiagnosed for over a decade before the first symptoms. Optical coherence tomography (OCT) is now common and widely available and has been used to image the retina of AD patients and healthy controls to search for biomarkers of neurodegeneration. However, early diagnosis tools would need to rely on images of patients in early AD stages, which are not available due to late diagnosis. To shed light on how to overcome this obstacle, we resort to 57 wild-type mice and 57 triple-transgenic mouse model of AD to train a network with mice aged 3, 4, and 8 months and classify mice at the ages of 1, 2, and 12 months. To this end, we computed fundus images from OCT data and trained a convolution neural network (CNN) to classify those into the wild-type or transgenic group. CNN performance accuracy ranged from 80 to 88% for mice out of the training group’s age, raising the possibility of diagnosing AD before the first symptoms through the non-invasive imaging of the retina.

Characterization of the retinal changes of the 3×Tg-AD mouse model of Alzheimer’s disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder whose diagnosis remains a notable challenge. The literature suggests that cerebral changes precede AD symptoms by over two decades, implying a significantly advanced stage of AD by the time it is usually diagnosed. In the study herein, texture analysis was applied to computed optical coherence tomography ocular fundus images to identify differences between a group of the transgenic mouse model of the Alzheimer’s disease (3×Tg-AD) and a group of wild-type mice, at the ages of one and two-months-old. A substantial difference between groups was found at both time-points across all neuroretina’s layers. Here, the inner nuclear layer stands out both in the level of statistically significant differences and on the extension of these differences which span through the imaged area. Also, the progression of AD is suggested to be spotted by texture analysis as demonstrated by the significant difference found in the inner plexiform and the outer nuclear layers from the age of one to the age of two-months-old. These findings demonstrate the potential of the use of the retina and texture analysis to the diagnosis of AD and monitor AD progression. Besides, the differences between groups found in this study suggest that the 3×Tg-AD model may be inappropriate to study early changes associated with the AD and other animal models should be tested following the same path and rationale. Moreover, these results also suggest that the human genes present in these transgenic mice may have an impact on the neurodevelopment of offspring which would justify the significant changes found at the age of one-month-old.

Retinal texture biomarkers may help to discriminate between Alzheimer's, Parkinson's, and healthy controls

A top priority in biomarker development for Alzheimer's disease (AD) and Parkinson's disease (PD) is the focus on early diagnosis, where the use of the retina is a promising avenue of research. We computed fundus images from optical coherence tomography (OCT) data and analysed the structural arrangement of the retinal tissue using texture metrics. We built clinical class classification models to distinguish between healthy controls (HC), AD, and PD, using machine learning (support vector machines). Median sensitivity is 88.7%, 79.5% and 77.8%, for HC, AD, and PD eyes, respectively. When the same subject has the same classification for both eyes, 94.4% (median) of the classifications are correct. A significant amount of information discriminating between multiple neurodegenerative states is conveyed by OCT imaging of the human retina, even when differences in thickness are not yet present. This technique may allow for simultaneously diagnosing Alzheimer's and Parkinson's diseases.

Three-dimensional segmentation and reconstruction of the retinal vasculature from spectral-domain optical coherence tomography

We reconstruct the three-dimensional shape and location of the retinal vascular network from commercial spectral-domain (SD) optical coherence tomography (OCT) data. The two-dimensional location of retinal vascular network on the eye fundus is obtained through support vector machines classification of properly defined fundus images from OCT data, taking advantage of the fact that on standard SD-OCT, the incident light beam is absorbed by hemoglobin, creating a shadow on the OCT signal below each perfused vessel. The depth-wise location of the vessel is obtained as the beginning of the shadow. The classification of crossovers and bifurcations within the vascular network is also addressed. We illustrate the feasibility of the method in terms of vessel caliber estimation and the accuracy of bifurcations and crossovers classification.