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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

ISBN: 978-989-758-212-7

Keyword(s): Granger Causality, Phase Slope Index, Graph Theory, Complex Network, Ageing, Magnetoencephalography.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics

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 th e resting state of the brain. (More)

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Paper citation in several formats:
Shumbayawonda, E.; Fernández, A.; Escudero, J.; Hughes , M. and Abásolo, D. (2017). Characterisation of Resting Brain Network Topologies across the Human Lifespan with Magnetoencephalogram Recordings: A Phase Slope Index and Granger Causality Comparison Study.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 118-125. DOI: 10.5220/0006104201180125

@conference{biosignals17,
author={Elizabeth Shumbayawonda. and Alberto Fernández. and Javier Escudero. and Michael Pycraft Hughes . and Daniel Abásolo.},
title={Characterisation of Resting Brain Network Topologies across the Human Lifespan with Magnetoencephalogram Recordings: A Phase Slope Index and Granger Causality Comparison Study},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={118-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006104201180125},
isbn={978-989-758-212-7},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Characterisation of Resting Brain Network Topologies across the Human Lifespan with Magnetoencephalogram Recordings: A Phase Slope Index and Granger Causality Comparison Study
SN - 978-989-758-212-7
AU - Shumbayawonda, E.
AU - Fernández, A.
AU - Escudero, J.
AU - Hughes , M.
AU - Abásolo, D.
PY - 2017
SP - 118
EP - 125
DO - 10.5220/0006104201180125

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