Information transfer efficiency during rest and task a functional connectome approach

Fumihiko Taya, Yu Sun, Nitish V Thakor, Anastasios Bezerianos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The brain consists of a number of sub-networks dedicated to several perceptual/cognitive functions, and allocates neural resources depending on cognitive demands. Recent studies on resting-state functional connectivity have shown competitive patterns of the functional sub-networks: 'task-negative' default mode networks and 'task-positive' networks. In this study, we employed the functional connectome approach to study the brain functional networks derived from fMRI data. Several graph theoretical measurements were employed to quantitatively investigate differences in global and local information transfer efficiency calculated between rest and task experimental conditions. Our results have suggested that normalized clustering coefficient was larger during rest compared to task, indicating more local efficiency of information transfer during rest, while small-worldness was preserved. In addition, the betweenness centrality of nodes was larger for task than rest at the orbital part of right superior frontal gyrus, the orbital part of right middle frontal gyrus and right olfactory cortex. In contrast, this parameter was larger for rest at left fusiform gyrus. As a consequence of this analysis, we show that graph theoretical measurements can be powerful biomarkers for quantifying cognitive states considering efficiency of information transfer, which can differ based on cognitive needs.

Original languageEnglish (US)
Title of host publicationIEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages101-104
Number of pages4
ISBN (Print)9781479923465
DOIs
StatePublished - Dec 9 2014
Externally publishedYes
Event10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014 - Lausanne, Switzerland
Duration: Oct 22 2014Oct 24 2014

Other

Other10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014
CountrySwitzerland
CityLausanne
Period10/22/1410/24/14

Fingerprint

Brain
Biomarkers
Magnetic Resonance Imaging

Keywords

  • fMRI
  • functional connectivity
  • graph theory
  • oddball task
  • resting-state
  • small-world network

ASJC Scopus subject areas

  • Hardware and Architecture
  • Biomedical Engineering

Cite this

Taya, F., Sun, Y., Thakor, N. V., & Bezerianos, A. (2014). Information transfer efficiency during rest and task a functional connectome approach. In IEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings (pp. 101-104). [6981655] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS.2014.6981655

Information transfer efficiency during rest and task a functional connectome approach. / Taya, Fumihiko; Sun, Yu; Thakor, Nitish V; Bezerianos, Anastasios.

IEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 101-104 6981655.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Taya, F, Sun, Y, Thakor, NV & Bezerianos, A 2014, Information transfer efficiency during rest and task a functional connectome approach. in IEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings., 6981655, Institute of Electrical and Electronics Engineers Inc., pp. 101-104, 10th IEEE Biomedical Circuits and Systems Conference, BioCAS 2014, Lausanne, Switzerland, 10/22/14. https://doi.org/10.1109/BioCAS.2014.6981655
Taya F, Sun Y, Thakor NV, Bezerianos A. Information transfer efficiency during rest and task a functional connectome approach. In IEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 101-104. 6981655 https://doi.org/10.1109/BioCAS.2014.6981655
Taya, Fumihiko ; Sun, Yu ; Thakor, Nitish V ; Bezerianos, Anastasios. / Information transfer efficiency during rest and task a functional connectome approach. IEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 101-104
@inproceedings{bf713f2c7ae64b05978359ee5b73b5ee,
title = "Information transfer efficiency during rest and task a functional connectome approach",
abstract = "The brain consists of a number of sub-networks dedicated to several perceptual/cognitive functions, and allocates neural resources depending on cognitive demands. Recent studies on resting-state functional connectivity have shown competitive patterns of the functional sub-networks: 'task-negative' default mode networks and 'task-positive' networks. In this study, we employed the functional connectome approach to study the brain functional networks derived from fMRI data. Several graph theoretical measurements were employed to quantitatively investigate differences in global and local information transfer efficiency calculated between rest and task experimental conditions. Our results have suggested that normalized clustering coefficient was larger during rest compared to task, indicating more local efficiency of information transfer during rest, while small-worldness was preserved. In addition, the betweenness centrality of nodes was larger for task than rest at the orbital part of right superior frontal gyrus, the orbital part of right middle frontal gyrus and right olfactory cortex. In contrast, this parameter was larger for rest at left fusiform gyrus. As a consequence of this analysis, we show that graph theoretical measurements can be powerful biomarkers for quantifying cognitive states considering efficiency of information transfer, which can differ based on cognitive needs.",
keywords = "fMRI, functional connectivity, graph theory, oddball task, resting-state, small-world network",
author = "Fumihiko Taya and Yu Sun and Thakor, {Nitish V} and Anastasios Bezerianos",
year = "2014",
month = "12",
day = "9",
doi = "10.1109/BioCAS.2014.6981655",
language = "English (US)",
isbn = "9781479923465",
pages = "101--104",
booktitle = "IEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Information transfer efficiency during rest and task a functional connectome approach

AU - Taya, Fumihiko

AU - Sun, Yu

AU - Thakor, Nitish V

AU - Bezerianos, Anastasios

PY - 2014/12/9

Y1 - 2014/12/9

N2 - The brain consists of a number of sub-networks dedicated to several perceptual/cognitive functions, and allocates neural resources depending on cognitive demands. Recent studies on resting-state functional connectivity have shown competitive patterns of the functional sub-networks: 'task-negative' default mode networks and 'task-positive' networks. In this study, we employed the functional connectome approach to study the brain functional networks derived from fMRI data. Several graph theoretical measurements were employed to quantitatively investigate differences in global and local information transfer efficiency calculated between rest and task experimental conditions. Our results have suggested that normalized clustering coefficient was larger during rest compared to task, indicating more local efficiency of information transfer during rest, while small-worldness was preserved. In addition, the betweenness centrality of nodes was larger for task than rest at the orbital part of right superior frontal gyrus, the orbital part of right middle frontal gyrus and right olfactory cortex. In contrast, this parameter was larger for rest at left fusiform gyrus. As a consequence of this analysis, we show that graph theoretical measurements can be powerful biomarkers for quantifying cognitive states considering efficiency of information transfer, which can differ based on cognitive needs.

AB - The brain consists of a number of sub-networks dedicated to several perceptual/cognitive functions, and allocates neural resources depending on cognitive demands. Recent studies on resting-state functional connectivity have shown competitive patterns of the functional sub-networks: 'task-negative' default mode networks and 'task-positive' networks. In this study, we employed the functional connectome approach to study the brain functional networks derived from fMRI data. Several graph theoretical measurements were employed to quantitatively investigate differences in global and local information transfer efficiency calculated between rest and task experimental conditions. Our results have suggested that normalized clustering coefficient was larger during rest compared to task, indicating more local efficiency of information transfer during rest, while small-worldness was preserved. In addition, the betweenness centrality of nodes was larger for task than rest at the orbital part of right superior frontal gyrus, the orbital part of right middle frontal gyrus and right olfactory cortex. In contrast, this parameter was larger for rest at left fusiform gyrus. As a consequence of this analysis, we show that graph theoretical measurements can be powerful biomarkers for quantifying cognitive states considering efficiency of information transfer, which can differ based on cognitive needs.

KW - fMRI

KW - functional connectivity

KW - graph theory

KW - oddball task

KW - resting-state

KW - small-world network

UR - http://www.scopus.com/inward/record.url?scp=84920532099&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920532099&partnerID=8YFLogxK

U2 - 10.1109/BioCAS.2014.6981655

DO - 10.1109/BioCAS.2014.6981655

M3 - Conference contribution

AN - SCOPUS:84920532099

SN - 9781479923465

SP - 101

EP - 104

BT - IEEE 2014 Biomedical Circuits and Systems Conference, BioCAS 2014 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -