The Cognitive Connectome in Healthy Aging
(2021) In Frontiers in Aging Neuroscience 13.- Abstract
Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups. Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph... (More)
Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups. Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures. Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups. Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.
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- author
- Garcia-Cabello, Eloy ; Gonzalez-Burgos, Lissett ; Pereira, Joana B. LU ; Hernández-Cabrera, Juan Andres ; Westman, Eric ; Volpe, Giovanni ; Barroso, José and Ferreira, Daniel
- organization
- publishing date
- 2021-08-18
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- aging, cognition, compensation, connectome, differentiation, graph theory
- in
- Frontiers in Aging Neuroscience
- volume
- 13
- article number
- 694254
- publisher
- Frontiers Media S. A.
- external identifiers
-
- pmid:34489673
- scopus:85114372201
- ISSN
- 1663-4365
- DOI
- 10.3389/fnagi.2021.694254
- language
- English
- LU publication?
- yes
- id
- bf04b4d9-ca71-4b6e-8540-b63e7fb125b8
- date added to LUP
- 2021-10-04 12:03:34
- date last changed
- 2025-03-09 18:51:04
@article{bf04b4d9-ca71-4b6e-8540-b63e7fb125b8,
abstract = {{<p>Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups. Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures. Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups. Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.</p>}},
author = {{Garcia-Cabello, Eloy and Gonzalez-Burgos, Lissett and Pereira, Joana B. and Hernández-Cabrera, Juan Andres and Westman, Eric and Volpe, Giovanni and Barroso, José and Ferreira, Daniel}},
issn = {{1663-4365}},
keywords = {{aging; cognition; compensation; connectome; differentiation; graph theory}},
language = {{eng}},
month = {{08}},
publisher = {{Frontiers Media S. A.}},
series = {{Frontiers in Aging Neuroscience}},
title = {{The Cognitive Connectome in Healthy Aging}},
url = {{http://dx.doi.org/10.3389/fnagi.2021.694254}},
doi = {{10.3389/fnagi.2021.694254}},
volume = {{13}},
year = {{2021}},
}