Cargando…

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal o...

Descripción completa

Detalles Bibliográficos
Autores principales: Iordan, Alexandru D., Cooke, Katherine A., Moored, Kyle D., Katz, Benjamin, Buschkuehl, Martin, Jaeggi, Susanne M., Jonides, John, Peltier, Scott J., Polk, Thad A., Reuter-Lorenz, Patricia A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758500/
https://www.ncbi.nlm.nih.gov/pubmed/29354048
http://dx.doi.org/10.3389/fnagi.2017.00419
_version_ 1783290999885266944
author Iordan, Alexandru D.
Cooke, Katherine A.
Moored, Kyle D.
Katz, Benjamin
Buschkuehl, Martin
Jaeggi, Susanne M.
Jonides, John
Peltier, Scott J.
Polk, Thad A.
Reuter-Lorenz, Patricia A.
author_facet Iordan, Alexandru D.
Cooke, Katherine A.
Moored, Kyle D.
Katz, Benjamin
Buschkuehl, Martin
Jaeggi, Susanne M.
Jonides, John
Peltier, Scott J.
Polk, Thad A.
Reuter-Lorenz, Patricia A.
author_sort Iordan, Alexandru D.
collection PubMed
description Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults.
format Online
Article
Text
id pubmed-5758500
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-57585002018-01-19 Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training Iordan, Alexandru D. Cooke, Katherine A. Moored, Kyle D. Katz, Benjamin Buschkuehl, Martin Jaeggi, Susanne M. Jonides, John Peltier, Scott J. Polk, Thad A. Reuter-Lorenz, Patricia A. Front Aging Neurosci Neuroscience Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA) and 20 older adults (OA) were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of cognitive transfer in both younger and older adults. Frontiers Media S.A. 2018-01-04 /pmc/articles/PMC5758500/ /pubmed/29354048 http://dx.doi.org/10.3389/fnagi.2017.00419 Text en Copyright © 2018 Iordan, Cooke, Moored, Katz, Buschkuehl, Jaeggi, Jonides, Peltier, Polk and Reuter-Lorenz. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Iordan, Alexandru D.
Cooke, Katherine A.
Moored, Kyle D.
Katz, Benjamin
Buschkuehl, Martin
Jaeggi, Susanne M.
Jonides, John
Peltier, Scott J.
Polk, Thad A.
Reuter-Lorenz, Patricia A.
Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training
title Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training
title_full Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training
title_fullStr Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training
title_full_unstemmed Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training
title_short Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training
title_sort aging and network properties: stability over time and links with learning during working memory training
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758500/
https://www.ncbi.nlm.nih.gov/pubmed/29354048
http://dx.doi.org/10.3389/fnagi.2017.00419
work_keys_str_mv AT iordanalexandrud agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT cookekatherinea agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT mooredkyled agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT katzbenjamin agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT buschkuehlmartin agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT jaeggisusannem agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT jonidesjohn agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT peltierscottj agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT polkthada agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining
AT reuterlorenzpatriciaa agingandnetworkpropertiesstabilityovertimeandlinkswithlearningduringworkingmemorytraining