Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics

TL;DR

This study reveals how functional brain network organization relates to cognitive reserve in aging, showing that individuals with higher CR have more efficient, less synchronized networks during memory tasks, with implications for understanding brain aging.

Contribution

It demonstrates the relationship between network topology, dynamics, and cognitive reserve, highlighting differences in brain network features between high and low CR individuals during memory tasks.

Findings

High CR individuals show lower network synchronization during tasks.

Network features like shortest path and outreach differ with CR levels.

Entropy correlates positively with network strength and clustering.

Abstract

We investigated how the organization of functional brain networks was related to cognitive reserve (CR) during a memory task in healthy aging. We obtained the magnetoencephalographic functional networks of 20 elders with a high or low CR level to analyse the differences at network features. We reported a negative correlation between synchronization of the whole network and CR, and observed differences both at the node and at the network level in: the average shortest path and the network outreach. Individuals with high CR required functional networks with lower links to successfully carry out the memory task. These results may indicate that those individuals with low CR level exhibited a dual pattern of compensation and network impairment, since their functioning was more energetically costly to perform the task as the high CR group. Additionally, we evaluated how the dynamical properties of the different brain regions were correlated to the network parameters obtaining that entropy was positively correlated with the strength and clustering coefficient, while complexity behaved conversely. Consequently, highly connected nodes of the functional networks showed a more stochastic and less complex signal. We consider that network approach may be a relevant tool to better understand brain functioning in aging.