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Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models
During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs‐fMRI) data showing a link between aging and the increase of between‐networks connectivity. However,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764474/ https://www.ncbi.nlm.nih.gov/pubmed/34783122 http://dx.doi.org/10.1002/hbm.25714 |
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author | Moretto, Manuela Silvestri, Erica Zangrossi, Andrea Corbetta, Maurizio Bertoldo, Alessandra |
author_facet | Moretto, Manuela Silvestri, Erica Zangrossi, Andrea Corbetta, Maurizio Bertoldo, Alessandra |
author_sort | Moretto, Manuela |
collection | PubMed |
description | During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs‐fMRI) data showing a link between aging and the increase of between‐networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data‐driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs‐fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph‐based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process. |
format | Online Article Text |
id | pubmed-8764474 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87644742022-01-21 Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models Moretto, Manuela Silvestri, Erica Zangrossi, Andrea Corbetta, Maurizio Bertoldo, Alessandra Hum Brain Mapp Research Articles During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs‐fMRI) data showing a link between aging and the increase of between‐networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data‐driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs‐fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph‐based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process. John Wiley & Sons, Inc. 2021-11-15 /pmc/articles/PMC8764474/ /pubmed/34783122 http://dx.doi.org/10.1002/hbm.25714 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles Moretto, Manuela Silvestri, Erica Zangrossi, Andrea Corbetta, Maurizio Bertoldo, Alessandra Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models |
title | Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models |
title_full | Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models |
title_fullStr | Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models |
title_full_unstemmed | Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models |
title_short | Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models |
title_sort | unveiling whole‐brain dynamics in normal aging through hidden markov models |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764474/ https://www.ncbi.nlm.nih.gov/pubmed/34783122 http://dx.doi.org/10.1002/hbm.25714 |
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