Paper Info
Title
Characterisation Of Resting Brain Network Topologies Across The Human Lifespan With Magnetoencephalogram Recordings: A Phase Slope Index And Granger Causality Comparison Study
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.
Year
DOI
Venue
2017
10.5220/0006104201180125
PROCEEDINGS OF THE 10TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 4: BIOSIGNALS
Keywords
Field
DocType
Granger Causality, Phase Slope Index, Graph Theory, Complex Network, Ageing, Magnetoencephalography
Graph theory,Random graph,Pattern recognition,Computer science,Granger causality,Resting state fMRI,Network topology,Betweenness centrality,Artificial intelligence,Network analysis,Clustering coefficient
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Elizabeth Shumbayawonda141.14
Alberto Fernández25311.82
Escudero Javier317427.45
Michael Pycraft Hughes441.14
Daniel Abásolo525326.22