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

Detalhes bibliográficos
Autor(a) principal: Guimarães, Pedro
Data de Publicação: 2022
Outros Autores: Serranho, Pedro, Martins, João, Moreira, Paula I., Ambrósio, António Francisco, Castelo-Branco, Miguel, Bernardes, Rui
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.2/12927
Resumo: 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.
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spelling Retinal aging in 3× Tg-AD mice model of Alzheimer's diseaseAgingArtificial intelligenceAge-gapAlzheimer’s diseaseDeep learningAnimal modelRetinaOptical coherence tomographyThe 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.This study was supported by The Portuguese Foundation for Science and Technology (FCT) through PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and by FEDER-COMPETE through POCI-01-0145-FEDER-028039.Repositório AbertoGuimarães, PedroSerranho, PedroMartins, JoãoMoreira, Paula I.Ambrósio, António FranciscoCastelo-Branco, MiguelBernardes, Rui2023-01-03T15:39:22Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.2/12927eng10.3389/fnagi.2022.832195info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-16T15:43:28Zoai:repositorioaberto.uab.pt:10400.2/12927Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:51:55.334316Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
title Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
spellingShingle Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
Guimarães, Pedro
Aging
Artificial intelligence
Age-gap
Alzheimer’s disease
Deep learning
Animal model
Retina
Optical coherence tomography
title_short Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
title_full Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
title_fullStr Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
title_full_unstemmed Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
title_sort Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
author Guimarães, Pedro
author_facet Guimarães, Pedro
Serranho, Pedro
Martins, João
Moreira, Paula I.
Ambrósio, António Francisco
Castelo-Branco, Miguel
Bernardes, Rui
author_role author
author2 Serranho, Pedro
Martins, João
Moreira, Paula I.
Ambrósio, António Francisco
Castelo-Branco, Miguel
Bernardes, Rui
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Aberto
dc.contributor.author.fl_str_mv Guimarães, Pedro
Serranho, Pedro
Martins, João
Moreira, Paula I.
Ambrósio, António Francisco
Castelo-Branco, Miguel
Bernardes, Rui
dc.subject.por.fl_str_mv Aging
Artificial intelligence
Age-gap
Alzheimer’s disease
Deep learning
Animal model
Retina
Optical coherence tomography
topic Aging
Artificial intelligence
Age-gap
Alzheimer’s disease
Deep learning
Animal model
Retina
Optical coherence tomography
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01T00:00:00Z
2023-01-03T15:39:22Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.2/12927
url http://hdl.handle.net/10400.2/12927
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3389/fnagi.2022.832195
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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