Authors:
Elizabeth Shumbayawonda
1
;
Alberto Fernández
2
;
Javier Escudero
3
;
Michael Pycraft Hughes
1
and
Daniel Abásolo
1
Affiliations:
1
University of Surrey, United Kingdom
;
2
Laboratorio UPM-UCM de Neurociencia Cognitiva y Computacional, Spain
;
3
University of Edinburgh, United Kingdom
Keyword(s):
Granger Causality, Phase Slope Index, Graph Theory, Complex Network, Ageing, Magnetoencephalography.
Abstract:
This study focuses on the resting state network analysis of the brain, as well as how these networks change
both in topology and location throughout life. The magnetoencephalogram (MEG) background activity from
220 healthy volunteers (age 7-84 years), was analysed combining complex network analysis principles of
graph theory with both linear and non-linear methods to evaluate the changes in the brain. Granger Causality
(GC) (linear method) and Phase Slope Index (PSI) (non-linear method) were used to observe the connectivity
in the brain during rest, and as a function of age by analysing the degree, clustering coefficient, efficiency,
betweenness, modularity and maximised modularity of the observed complex brain networks. Our results
showed that GC showed little linear causal activity in the brain at rest, with small world topology, while PSI
showed little information flow in the brain, with random network topology. However, both analyses produced
complementary results pertaining to t
he resting state of the brain.
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