Characterisation of Resting Brain Network Topologies across the Human Lifespan with Magnetoencephalogram Recordings: a Phase Slope Index and Granger Causality Comparison Study

Elizabeth Shumbayawonda, Alberto Fernandez, Javier Escudero, Michael Pycraft Hughes, Daniel Abasolo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 the resting state of the brain.
Original languageEnglish
Title of host publicationProceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017)
Pages118-125
Number of pages8
Volume4
EditionBIOSIGNALS
DOIs
Publication statusPublished - Feb 2017
EventBIOSIGNALS2017 - 10th International Conference on Bioinspired Systems and Signal Modelling - Porto, Portugal
Duration: 21 Feb 201723 Feb 2017

Conference

ConferenceBIOSIGNALS2017 - 10th International Conference on Bioinspired Systems and Signal Modelling
Country/TerritoryPortugal
CityPorto
Period21/02/1723/02/17

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