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

Publication . Ferreira, Hugo; Martins, João; Nunes, Ana; Moreira, Paula I.; Castelo-Branco, Miguel; Ambrósio, António F.; Serranho, Pedro; Bernardes, Rui

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.

2020Journal article

Open access

Longitudinal normative OCT retinal thickness data for wild-type mice, and characterization of changes in the 3×Tg-AD mice model of Alzheimer's disease

Publication . Ferreira, Hugo; Martins, João; Moreira, Paula I.; Ambrósio, António F.; Castelo-Branco, Miguel; Serranho, Pedro; Bernardes, Rui

Mice are widely used as models for many diseases, including eye and neurodegenerative diseases. However, there is a lack of normative data for retinal thickness over time, especially at young ages. In this work, we present a normative thickness database from one to four-months-old, for nine layers/layer-aggregates, including the total retinal thickness, obtained from the segmentation of spectral-domain optical coherence tomography (SD-OCT) data from the C57BL6/129S mouse strain. Based on fifty-seven mice, this normative database provides an opportunity to study the ageing of control mice and characterize disease models' ageing, such as the triple transgenic mouse model of Alzheimer's disease (3×Tg-AD) used in this work. We report thickness measurements, the differences in thickness per layer, demonstrate a nasal-temporal asymmetry, and the variation of thickness as a function to the distance to the optic disc center. Significant differences were found between the transgenic group's thickness and the normative database for the entire period covered in this study. Even though it is well accepted that retinal nerve fiber layer (RNFL) thinning is a hallmark of neurodegeneration, our results show a thicker RNFL-GCL (RNFL-Ganglion cell layer) aggregate for the 3×Tg-AD mice until four-months-old.

2021Journal article

Open access

Shedding light on early central nervous system changes for Alzheimer’s disease through the retina: an animal study

Publication . Bernardes, Rui; Ferreira, Hugo; Guimarães, Pedro; Serranho, Pedro

The World Health Organization (WHO) 2015 projections estimated 75.6 million people living with dementia in 2030, an update from the 66 million estimated in 2013. These figures account for all types of dementia, but Alzheimer’s disease stands out as the most common estimated type, representing 60% to 80% of the cases. An increasing number of research groups adopted the approach of using the retina as a window to the brain. Besides being the visible part of the central nervous system, the retina is readily available through non-invasive imaging techniques such as optical coherence tomography (OCT). Moreover, cumulative evidence indicates that neurodegenerative diseases can also affect the retina. In the work reported herein, we imaged the retina of wild-type and the triple-transgenic mouse model of Alzheimer’s disease, at the ages of one-, two-, three-, four-, eight-, twelve- and sixteen-months-old, by OCT and segmented gathered data using a developed convolutional neural network into distinct layers. Group differences through texture analysis of computed fundus images for five layers of the retina, normative retinal thickness data throughout the observation period of the ageing mice, and findings related to the estimation of the ageing effect of the human genes present in the transgenic group, as well as the classification of individual fundus images through convolutional neural networks, will be presented and thoroughly discussed in the Special Session on ”New Developments in Imaging for Ocular and Neurodegenerative Disorders”.

2022Journal article

Restricted access

Aplicação de máquinas de vector suporte para classificação de ratos transgénicos através de imagem da retina

Publication . Valentim, Érick Braga; Serranho, Pedro; Bernardes, Rui Manuel Dias Cortesão dos Santos

O objetivo deste trabalho consistiu na criação de modelos de aprendizagem supervisionada baseados nas técnicas de Support Vector Machine (SVM) e Support Vector Machine com informação privilegiada (SVM+) capazes de distinguir entre ratos saudáveis (C) e transgénicos (D) por meio de análise de textura da imagem de tomografia de coerência óptica (OCT) de retinas do olho direito. A amostra é composta por 74 ratos, sendo 40 saudáveis e 34 transgénicos. A tomografia de coerência óptica foi utilizada para obtenção da imagem da retina dos ratos que, por sua vez, foi dividida em 4 quadrantes. A partir destes, obteve-se uma imagem de fundo 2D e foram aplicados 20 indicadores de análise de textura de imagem de fundo, usados como features para o modelo SVM. As features com maior capacidade de separação entre grupos e que possuem coeficiente de correlação inferior a 0,7 entre elas foram Inertia (primeiro, segundo e quarto quadrantes), INN (Inverse difference normalized; terceiro quadrante), IMC2 (Information measure of correlation; terceiro quadrante) e ClusterShade (terceiro quadrante). Considerando as 6 features mais relevantes foram criados os modelos SVM e SVM+ cujos parâmetros foram afinados de maneira a obter os modelos com a melhor precisão na classificação dos ratos nas categorias saudável e transgénico. A técnica de validação cruzada em 5 grupos foi utilizada para validar os resultados dos modelos criados. Tanto para o conjunto de teste como para o conjunto de dados total o modelo SVM obteve 100% precisão, enquanto que a precisão obtida pelo modelo SVM+ foi de 93,33% (erro de apenas 1 caso em 15 – conjunto de teste) na classificação dos dados do conjunto de teste e 98,65% (erro de apenas 1 caso em 74 – conjunto de dados total) no conjunto de dados total.

2020-07-22Master thesis

Open access

Retinal aging in 3× Tg-AD mice model of Alzheimer's disease

Publication . Guimarães, Pedro; Serranho, Pedro; Martins, João; Moreira, Paula I.; Ambrósio, António Francisco; Castelo-Branco, Miguel; Bernardes, Rui

The retina, as part of the central nervous system (CNS), can be the perfect target for in vivo, in situ, and noninvasive neuropathology diagnosis and assessment of therapeutic efficacy. It has long been established that several age-related brain changes are more pronounced in Alzheimer’s disease (AD). Nevertheless, in the retina such link is still under-explored. This study investigates the differences in the aging of the CNS through the retina of 3×Tg-AD and wild-type mice. A dedicated optical coherence tomograph imaged mice’s retinas for 16 months. Two neural networks were developed to model independently each group’s ages and were then applied to an independent set containing images fromboth groups. Our analysis shows amean absolute error of 0.875±1.1×10−2 and 1.112 ± 1.4 × 10−2 months, depending on training group. Our deep learning approach appears to be a reliable retinal OCT aging marker. We show that retina aging is distinct in the two classes: the presence of the three mutated human genes in the mouse genome has an impact on the aging of the retina. For mice over 4 months-old, transgenic mice consistently present a negative retina age-gap when compared to wildtype mice, regardless of training set. This appears to contradict AD observations in the brain. However, the ‘black-box” nature of deep-learning implies that one cannot infer reasoning. We can only speculate that some healthy age-dependent neural adaptations may be altered in transgenic animals.

2022Journal article

Open access