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High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model
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journal.pcbi.1010431.pdf | Published version | 2.21 MB | Adobe PDF | View/Open |
Title: | High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model |
Authors: | Gatica, M E Rosas, F A M Mediano, P Diez, I P Swinnen, S Orio, P Cofré, R M Cortes, J |
Item Type: | Journal Article |
Abstract: | The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the differences in high-order functional interactions between age groups can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modeling more complex forms of pathological ageing or cognitive deficits. |
Issue Date: | 2-Sep-2022 |
Date of Acceptance: | 23-Jul-2022 |
URI: | http://hdl.handle.net/10044/1/102229 |
DOI: | 10.1371/journal.pcbi.1010431 |
ISSN: | 1553-734X |
Publisher: | Public Library of Science (PLoS) |
Start Page: | 1 |
End Page: | 21 |
Journal / Book Title: | PLoS Computational Biology |
Volume: | 18 |
Issue: | 9 |
Copyright Statement: | © 2022 Gatica et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Publication Status: | Published |
Conference Place: | United States |
Article Number: | e1010431 |
Online Publication Date: | 2022-09-02 |
Appears in Collections: | Department of Brain Sciences |
This item is licensed under a Creative Commons License