Longitudinal functional brain network reconfiguration in healthy aging

Abstract

Healthy aging is associated with changes in cognitive performance and functional brain organization. In fact, cross‐sectional studies imply lower modularity and significant heterogeneity in modular architecture across older subjects. Here, we used a longitudinal dataset consisting of four occasions of resting‐state‐fMRI and cognitive testing (spanning 4 years) in 150 healthy older adults. We applied a graph‐theoretic analysis to investigate the time‐evolving modular structure of the whole‐brain network, by maximizing the multilayer modularity across four time points. Global flexibility, which reflects the tendency of brain nodes to switch between modules across time, was significantly higher in healthy elderly than in a temporal null model. Further, global flexibility, as well as network‐specific flexibility of the default mode, frontoparietal control, and somatomotor networks, were significantly associated with age at baseline. These results indicate that older age is related to higher variability in modular organization. The temporal metrics were not associated with simultaneous changes in processing speed or learning performance in the context of memory encoding. Finally, this approach provides global indices for longitudinal change across a given time span and it may contribute to uncovering patterns of modular variability in healthy and clinical aging populations.

Strategies and cognitive reserve to preserve lexical production in aging

“…Moreover, these intrinsic networks evolve under the effect of age toward a decrease in network segregation, as has been observed in older participants [29,63]. A recent longitudinal study [117] performed on a 4-year period showed significant reconfiguration of these networks in healthy older adults, with significant network flexibility between modules, even if the authors did not find a correlation with cognitive performance. Other studies focused on variations in the activity of the default mode network with age.…”

Probabilistically Weighted Multilayer Networks disclose the link between default mode network instability and psychosis-like experiences in healthy adults

“…However, advances in the understanding of dynamic brain networks point toward the existence of multi-scale entities in the brain architecture [1][2][3][4][5][10][11][12][13]16 . These advances followed recent developments in network science [62][63][64][65] , and generally indicate the necessity to implement unbiased methods for detecting communities [66][67][68][69][70][71][72] , possibly controlling for multiple stochasticity in the community organization, like in multi-resolution approaches 32,73,74 . In fact, the choice of the resolution parameter γ is important since it regulates the number and size of communities in a network [14][15][16] .…”

Probabilistically Weighted Multilayer Networks Disclose the Link Between Default Mode Network Instability and Psychosis-Like Experiences in Healthy Adults

“…However, advances in the understanding of dynamic brain networks point toward the existence of multi-scale entities in the brain architecture [1][2][3][4][5][10][11][12][13]16 . These advances followed recent developments in network science [62][63][64][65] , and generally indicate the necessity to implement unbiased methods for detecting communities [66][67][68][69][70][71][72] , possibly controlling for multiple stochasticity in the community organization, like in multi-resolution approaches 32,73,74 . In fact, the choice of the resolution parameter γ is important since it regulates the number and size of communities in a network [14][15][16] .…”