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Related Experiment Video

Updated: Feb 15, 2026

Assessing Spatial Learning and Memory in Small Squamate Reptiles

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Published on: January 3, 2017

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

Alexandru D Iordan¹, Katherine A Cooke¹, Kyle D Moored²

¹Department of Psychology, University of Michigan, Ann Arbor, MI, United States.

Frontiers in Aging Neuroscience

|January 23, 2018

Summary

Healthy aging alters brain network stability and efficiency, with older adults showing reduced modularity and local efficiency. These age-related changes in functional brain networks were stable over time and showed limited associations with subsequent learning capacity.

Keywords:

functional connectivity graph theory intraclass correlation intrinsic activity reliability analysis

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Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

Published on: September 20, 2020

Area of Science:

Neuroscience
Cognitive Aging
Brain Network Analysis

Background:

Healthy aging is associated with changes in large-scale functional brain networks, including reduced modularity and local efficiency.
The long-term stability of these age-related network alterations and their impact on learning remain unclear.

Purpose of the Study:

To investigate age-related differences in resting-state functional brain networks.
To assess the temporal stability of these network configurations.
To examine the relationship between baseline network properties and subsequent learning.

Main Methods:

Resting-state functional magnetic resonance imaging (fMRI) data were collected from young adults (YA) and older adults (OA) across two sessions, two weeks apart.
Graph-theoretic analyses were employed to evaluate network properties such as modularity, local efficiency, and connectivity.
Working memory training was administered post-fMRI to assess learning rates.

Main Results:

Older adults exhibited lower brain-wide modularity and local efficiency compared to young adults, consistent with functional dedifferentiation.
These age-related effects on network properties were found to be replicable over time.
Specific network alterations in older adults included reduced within-network connectivity in the default-mode network and altered participation in the cingulo-opercular and somato-sensorimotor networks.
Baseline network properties showed a limited association with subsequent learning rates during working memory training.

Conclusions:

Aging impacts the stability and efficiency of functional brain networks, with observed changes being reliable over time.
Age-related alterations in specific brain networks may influence cognitive functions like learning.
Understanding these neural mechanisms is crucial for predicting training gains and cognitive transfer in aging populations.