Retinal aging in 3× Tg-AD mice model of Alzheimer's disease
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
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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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 |
format |
article |
status_str |
publishedVersion |
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 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799135110861160448 |