MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling

The progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modal...

ver descrição completa

Detalhes bibliográficos
Autores: Martí Juan, Gerard, Lorenzi, Marco, Piella Fenoy, Gemma
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/57503
Acesso em linha:http://hdl.handle.net/10230/57503
http://dx.doi.org/10.1016/j.neuroimage.2023.119892
Access Level:acceso abierto
Palavra-chave:Alzheimer’s disease
Longitudinal
Multimodal
Variational autoencoder
Recurrent neural network
Disease progression modelling
id ES_1d370da73da260ad4d30acaade6e8dfe
oai_identifier_str oai:recercat.cat:10230/57503
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
title MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
spellingShingle MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
Martí Juan, Gerard
Alzheimer’s disease
Longitudinal
Multimodal
Variational autoencoder
Recurrent neural network
Disease progression modelling
title_short MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
title_full MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
title_fullStr MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
title_full_unstemmed MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
title_sort MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling
dc.creator.none.fl_str_mv Martí Juan, Gerard
Lorenzi, Marco
Piella Fenoy, Gemma
author Martí Juan, Gerard
author_facet Martí Juan, Gerard
Lorenzi, Marco
Piella Fenoy, Gemma
author_role author
author2 Lorenzi, Marco
Piella Fenoy, Gemma
author2_role author
author
dc.subject.none.fl_str_mv Alzheimer’s disease
Longitudinal
Multimodal
Variational autoencoder
Recurrent neural network
Disease progression modelling
topic Alzheimer’s disease
Longitudinal
Multimodal
Variational autoencoder
Recurrent neural network
Disease progression modelling
description The progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with = 897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/57503
http://dx.doi.org/10.1016/j.neuroimage.2023.119892
url http://hdl.handle.net/10230/57503
http://dx.doi.org/10.1016/j.neuroimage.2023.119892
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv NeuroImage. 2023;268:119892.
https://www.github.com/GerardMJuan/RNN-VAE
info:eu-repo/grantAgreement/EC/H2020/848158
info:eu-repo/grantAgreement/ES/3PE/PCI2021-122044-2A
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404267652055040
spelling MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modellingMartí Juan, GerardLorenzi, MarcoPiella Fenoy, GemmaAlzheimer’s diseaseLongitudinalMultimodalVariational autoencoderRecurrent neural networkDisease progression modellingThe progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with = 897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.This work is supported by the European Union’s Horizon 2020 research and innovation programme (grant n◦ 848158). M. Lorenzi is supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) (ANR-19-P3IA-0002). G. Piella is supported by ICREA under the ICREA Academia programme. This publication is part of the project PCI2021-122044-2A, funded by the project ERA-NET NEURON Cofund2, by MCIN/AEI/10.13039/501100011033/ and by the European Union “NextGenerationEU”/PRTR. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.Elsevier202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/57503http://dx.doi.org/10.1016/j.neuroimage.2023.119892reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésNeuroImage. 2023;268:119892.https://www.github.com/GerardMJuan/RNN-VAEinfo:eu-repo/grantAgreement/EC/H2020/848158info:eu-repo/grantAgreement/ES/3PE/PCI2021-122044-2A© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/575032026-05-29T05:05:01Z
score 15,812429

Registos relacionados