Integration Versus Segregation in Functional Brain Networks

We propose a new methodology to evaluate the balance between segregation and integration in functional brain networks by using singular value decomposition techniques. By means of magnetoencephalography, we obtain the brain activity of a control group of 19 individuals during a memory task. Next, we...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2011-10, Vol.58 (10), p.3004-3007
Main Authors: Sendina-Nadal, I., Buldu, Javier M., Leyva, I., Bajo, R., Almendral, J. A., del-Pozo, F.
Format: Article
Language:English
Subjects:
Aged
Algorithms
Biomedical engineering
Brain
Brain - physiology
Brain Mapping - methods
Communities
Complex networks
functional brain networks
Humans
Magnetic resonance imaging
Magnetoencephalography
Memory - physiology
Nerve Net - physiology
Signal Processing, Computer-Assisted
singular value decomposition (SVD)
Studies
Synchronization
Task Performance and Analysis
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Summary:We propose a new methodology to evaluate the balance between segregation and integration in functional brain networks by using singular value decomposition techniques. By means of magnetoencephalography, we obtain the brain activity of a control group of 19 individuals during a memory task. Next, we project the node-to-node correlations into a complex network that is analyzed from the perspective of its modular structure encoded in the contribution matrix. In this way, we are able to study the role that nodes play I/O its community and to identify connector and local hubs. At the mesoscale level, the analysis of the contribution matrix allows us to measure the degree of overlapping between communities and quantify how far the functional networks are from the configuration that better balances the integrated and segregated activity.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2011.2161084