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

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.

Dynamical complexity as a proxy for the network degree distribution

“…The number N k of nodes having the same degree k, is given by the degree probability distribution P (k) as N k = N P (k). In order to address our hypothesis about the relationship between the changes in the dynamical properties of each single unit and the number of neighbors it has, we measure the Martín-Plastino-Rosso (MPR) statistical complexity [27][28][29] of ordinal patterns extracted from the signal produced by each dynamical unit, as a function of the node degree k i and the coupling strength d. The methods of analysis of time-series based on statistical complexity are gaining relevance in the last years as they provide an easily computable way to quantify the information carried by a signal [30][31][32], and have been applied to a wide variety of systems: from brain data [31,33], to climate data [34], or financial analysis [35].…”

State inequality, socioeconomic position and subjective cognitive decline in the United States

“…Cognitive stimulation is hypothesized to have a protective effect for cognition through promoting “cognitive reserve,” or the increased efficiency and capacity of neural networks in the presence of dementia pathology (Stern, 2009). Theoretically, cognitive stimulation is thought to allow for greater cognitive flexibility that allows an individual to continue to function well, even in the presence of dementia-related brain pathologies (Martínez et al, 2018, Meng and D’Arcy, 2012).…”

Characterization of Visuomotor/Imaginary Movements in EEG: An Information Theory and Complex Network Approach

“…In this manner some endeavors to use convenient techniques on the time series dynamics recreated from scalp EEG signals can be found in the literature [14][15][16][17]. Network theory is usually based on graph theory, probability theory, statistical mechanics, and dynamical systems [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”