TY - GEN
T1 - Characterisation of resting brain network topologies across the human lifespan with magnetoencephalogram recordings
T2 - 10th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2017 - Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017
AU - Shumbayawonda, Elizabeth
AU - Fernández, Alberto
AU - Escudero, Javier
AU - Hughes, Michael Pycraft
AU - Abásolo, Daniel
N1 - Publisher Copyright:
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Ageing
KW - Complex Network
KW - Granger Causality
KW - Graph Theory
KW - Magnetoencephalography
KW - Phase Slope Index
UR - http://www.scopus.com/inward/record.url?scp=85051830798&partnerID=8YFLogxK
U2 - 10.5220/0006104201180125
DO - 10.5220/0006104201180125
M3 - Conference contribution
AN - SCOPUS:85051830798
T3 - BIOSIGNALS 2017 - 10th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017
SP - 118
EP - 125
BT - BIOSIGNALS 2017 - 10th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017
A2 - Maciel, Carlos D.
A2 - Fred, Ana
A2 - Gamboa, Hugo
A2 - Vaz, Mario
Y2 - 21 February 2017 through 23 February 2017
ER -