Redundancy in functional brain connectivity from eeg recordings

Fabrizio De Vico Fallani, Jlenia Toppi, Claudia Di Lanzo, Giovanni Vecchiato, Laura Astolfi, Gianluca Borghini, Donatella Mattia, Febo Cincotti, Fabio Babiloni

Research output: Contribution to journalArticle

Abstract

The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p <0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p <0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5-12.5 Hz), which is known to be the most responsive channel during resting state conditions.

Original languageEnglish
Article number1250158
JournalInternational Journal of Bifurcation and Chaos
Volume22
Issue number7
DOIs
Publication statusPublished - Jul 2012

Fingerprint

Redundancy
Brain
Connectivity
Electroencephalography
Vertex of a graph
Random Networks
Resilience
Path Length
Random Graphs
Frequency bands
Damage
Scalar
Oscillation
Benchmark
Sensor
Path
Resources
Electroencephalogram
Methodology
Sensors

Keywords

  • brain connectivity
  • EEG
  • graph theory
  • multiple pathways
  • resting state

ASJC Scopus subject areas

  • Applied Mathematics
  • General
  • Engineering(all)
  • Modelling and Simulation

Cite this

Redundancy in functional brain connectivity from eeg recordings. / De Vico Fallani, Fabrizio; Toppi, Jlenia; Di Lanzo, Claudia; Vecchiato, Giovanni; Astolfi, Laura; Borghini, Gianluca; Mattia, Donatella; Cincotti, Febo; Babiloni, Fabio.

In: International Journal of Bifurcation and Chaos, Vol. 22, No. 7, 1250158, 07.2012.

Research output: Contribution to journalArticle

De Vico Fallani, Fabrizio ; Toppi, Jlenia ; Di Lanzo, Claudia ; Vecchiato, Giovanni ; Astolfi, Laura ; Borghini, Gianluca ; Mattia, Donatella ; Cincotti, Febo ; Babiloni, Fabio. / Redundancy in functional brain connectivity from eeg recordings. In: International Journal of Bifurcation and Chaos. 2012 ; Vol. 22, No. 7.
@article{d11b1e4511d0408c9c96353b409c0778,
title = "Redundancy in functional brain connectivity from eeg recordings",
abstract = "The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p <0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p <0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5-12.5 Hz), which is known to be the most responsive channel during resting state conditions.",
keywords = "brain connectivity, EEG, graph theory, multiple pathways, resting state",
author = "{De Vico Fallani}, Fabrizio and Jlenia Toppi and {Di Lanzo}, Claudia and Giovanni Vecchiato and Laura Astolfi and Gianluca Borghini and Donatella Mattia and Febo Cincotti and Fabio Babiloni",
year = "2012",
month = "7",
doi = "10.1142/S0218127412501581",
language = "English",
volume = "22",
journal = "International Journal of Bifurcation and Chaos in Applied Sciences and Engineering",
issn = "0218-1274",
publisher = "World Scientific Publishing Co. Pte Ltd",
number = "7",

}

TY - JOUR

T1 - Redundancy in functional brain connectivity from eeg recordings

AU - De Vico Fallani, Fabrizio

AU - Toppi, Jlenia

AU - Di Lanzo, Claudia

AU - Vecchiato, Giovanni

AU - Astolfi, Laura

AU - Borghini, Gianluca

AU - Mattia, Donatella

AU - Cincotti, Febo

AU - Babiloni, Fabio

PY - 2012/7

Y1 - 2012/7

N2 - The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p <0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p <0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5-12.5 Hz), which is known to be the most responsive channel during resting state conditions.

AB - The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p <0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p <0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5-12.5 Hz), which is known to be the most responsive channel during resting state conditions.

KW - brain connectivity

KW - EEG

KW - graph theory

KW - multiple pathways

KW - resting state

UR - http://www.scopus.com/inward/record.url?scp=84864818355&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864818355&partnerID=8YFLogxK

U2 - 10.1142/S0218127412501581

DO - 10.1142/S0218127412501581

M3 - Article

AN - SCOPUS:84864818355

VL - 22

JO - International Journal of Bifurcation and Chaos in Applied Sciences and Engineering

JF - International Journal of Bifurcation and Chaos in Applied Sciences and Engineering

SN - 0218-1274

IS - 7

M1 - 1250158

ER -