Model-based whole-brain perturbational landscape of neurodegenerative diseases
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- dc.contributor.author Sanz Perl, Yonatan
- dc.contributor.author Fittipaldi, Sol
- dc.contributor.author Gonzalez Campo, Cecilia
- dc.contributor.author Moguilner, Sebastián
- dc.contributor.author Cruzat Grand, Josefina, 1983-
- dc.contributor.author Fraile-Vazquez, Matias E.
- dc.contributor.author Herzog, Rubén
- dc.contributor.author Kringelbach, Morten L.
- dc.contributor.author Deco, Gustavo
- dc.contributor.author Prado, Pavel
- dc.contributor.author Ibañez, Agustin
- dc.contributor.author Tagliazucchi, Enzo
- dc.date.accessioned 2023-06-29T07:05:35Z
- dc.date.available 2023-06-29T07:05:35Z
- dc.date.issued 2023
- dc.description.abstract 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.
- dc.format.mimetype application/pdf
- dc.identifier.citation Sanz Perl Y, Fittipaldi S, Gonzalez Campo C, Moguilner S, Cruzat J, Fraile-Vazquez ME, Herzog R, Kringelbach ML, Deco G, Prado P, Ibanez A, Tagliazucchi E. Model-based whole-brain perturbational landscape of neurodegenerative diseases. Elife. 2023;12:e83970. DOI: 10.7554/eLife.83970
- dc.identifier.doi http://dx.doi.org/10.7554/eLife.83970
- dc.identifier.issn 2050-084X
- dc.identifier.uri http://hdl.handle.net/10230/57406
- dc.language.iso eng
- dc.publisher eLife
- dc.relation.ispartof eLife. 2023;12:e83970.
- dc.relation.isreferencedby https://tinyurl.com/27652jkz
- dc.rights Copyright 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.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Neuroscience
- dc.subject.keyword neurodegeneration
- dc.subject.keyword fMRI
- dc.subject.keyword whole-brain computational modelling
- dc.subject.keyword deep learning
- dc.title Model-based whole-brain perturbational landscape of neurodegenerative diseases
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion