Model-based whole-brain perturbational landscape of neurodegenerative diseases

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional co...

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Detalhes bibliográficos
Autores: Sanz Perl, Yonatan, Fittipaldi, Sol, Gonzalez Campo, Cecilia, Moguilner, Sebastián, Cruzat Grand, Josefina, 1983-, Fraile-Vazquez, Matias E., Herzog, Rubén, Kringelbach, Morten L., Deco, Gustavo, Prado, Pavel, Ibañez, Agustin, Tagliazucchi, Enzo
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/57406
Acesso em linha:http://hdl.handle.net/10230/57406
http://dx.doi.org/10.7554/eLife.83970
Access Level:acceso abierto
Palavra-chave:Neuroscience
neurodegeneration
fMRI
whole-brain computational modelling
deep learning
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spelling Model-based whole-brain perturbational landscape of neurodegenerative diseasesSanz Perl, YonatanFittipaldi, SolGonzalez Campo, CeciliaMoguilner, SebastiánCruzat Grand, Josefina, 1983-Fraile-Vazquez, Matias E.Herzog, RubénKringelbach, Morten L.Deco, GustavoPrado, PavelIbañez, AgustinTagliazucchi, EnzoNeuroscienceneurodegenerationfMRIwhole-brain computational modellingdeep learningThe treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.eLife202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/57406http://dx.doi.org/10.7554/eLife.83970reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraIngléseLife. 2023;12:e83970.https://tinyurl.com/27652jkzCopyright Sanz Perl et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/574062026-06-12T07:21:37Z
dc.title.none.fl_str_mv Model-based whole-brain perturbational landscape of neurodegenerative diseases
title Model-based whole-brain perturbational landscape of neurodegenerative diseases
spellingShingle Model-based whole-brain perturbational landscape of neurodegenerative diseases
Sanz Perl, Yonatan
Neuroscience
neurodegeneration
fMRI
whole-brain computational modelling
deep learning
title_short Model-based whole-brain perturbational landscape of neurodegenerative diseases
title_full Model-based whole-brain perturbational landscape of neurodegenerative diseases
title_fullStr Model-based whole-brain perturbational landscape of neurodegenerative diseases
title_full_unstemmed Model-based whole-brain perturbational landscape of neurodegenerative diseases
title_sort Model-based whole-brain perturbational landscape of neurodegenerative diseases
dc.creator.none.fl_str_mv Sanz Perl, Yonatan
Fittipaldi, Sol
Gonzalez Campo, Cecilia
Moguilner, Sebastián
Cruzat Grand, Josefina, 1983-
Fraile-Vazquez, Matias E.
Herzog, Rubén
Kringelbach, Morten L.
Deco, Gustavo
Prado, Pavel
Ibañez, Agustin
Tagliazucchi, Enzo
author Sanz Perl, Yonatan
author_facet Sanz Perl, Yonatan
Fittipaldi, Sol
Gonzalez Campo, Cecilia
Moguilner, Sebastián
Cruzat Grand, Josefina, 1983-
Fraile-Vazquez, Matias E.
Herzog, Rubén
Kringelbach, Morten L.
Deco, Gustavo
Prado, Pavel
Ibañez, Agustin
Tagliazucchi, Enzo
author_role author
author2 Fittipaldi, Sol
Gonzalez Campo, Cecilia
Moguilner, Sebastián
Cruzat Grand, Josefina, 1983-
Fraile-Vazquez, Matias E.
Herzog, Rubén
Kringelbach, Morten L.
Deco, Gustavo
Prado, Pavel
Ibañez, Agustin
Tagliazucchi, Enzo
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Neuroscience
neurodegeneration
fMRI
whole-brain computational modelling
deep learning
topic Neuroscience
neurodegeneration
fMRI
whole-brain computational modelling
deep learning
description The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.
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/10230/57406
http://dx.doi.org/10.7554/eLife.83970
url http://hdl.handle.net/10230/57406
http://dx.doi.org/10.7554/eLife.83970
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv eLife. 2023;12:e83970.
https://tinyurl.com/27652jkz
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv eLife
publisher.none.fl_str_mv eLife
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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