Training-induced changes in information transfer efficiency of the brain network: A functional connectome approach

Fumihiko Taya, Yu Sun, Gianluca Borghini, Pietro Aricò, Fabio Babiloni, Anastasios Bezerianos, Nitish V Thakor

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

Abstract

Training is a process to improve one's capacity or performance through the acquisition of knowledge or skills specific for the task. Although behavioral performance would be improved monotonically and reach a plateau as the learning progresses, neurophysiological process shows different pattern like an inverted U-shaped curve. One possible account for the phenomenon is that the brain first works hard to learn how to use specific task-relevant areas, followed by improvement of efficiency derived from disuse of irrelevant brain areas for good task performance. In this study, we employed the functional connectome approach to study the changes in global and local information transfer efficiency of the functional connectivity induced by training of a piloting task. Our results have demonstrated that global information transfer efficiency of the network, revealed by normalized characteristic path length in beta band, once decreased and then increased during the training sessions. We show that graph theoretical network metrics can be used as biomarkers for quantifying the degree of training progresses, in terms of efficiency, which can be differed based on cognitive proficiency of the brain.

Original languageEnglish (US)
Title of host publicationInternational IEEE/EMBS Conference on Neural Engineering, NER
PublisherIEEE Computer Society
Pages1028-1031
Number of pages4
Volume2015-July
ISBN (Print)9781467363891
DOIs
StatePublished - Jul 1 2015
Externally publishedYes
Event7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 - Montpellier, France
Duration: Apr 22 2015Apr 24 2015

Other

Other7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
CountryFrance
CityMontpellier
Period4/22/154/24/15

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Brain
Biomarkers

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

Cite this

Taya, F., Sun, Y., Borghini, G., Aricò, P., Babiloni, F., Bezerianos, A., & Thakor, N. V. (2015). Training-induced changes in information transfer efficiency of the brain network: A functional connectome approach. In International IEEE/EMBS Conference on Neural Engineering, NER (Vol. 2015-July, pp. 1028-1031). [7146802] IEEE Computer Society. https://doi.org/10.1109/NER.2015.7146802

Training-induced changes in information transfer efficiency of the brain network : A functional connectome approach. / Taya, Fumihiko; Sun, Yu; Borghini, Gianluca; Aricò, Pietro; Babiloni, Fabio; Bezerianos, Anastasios; Thakor, Nitish V.

International IEEE/EMBS Conference on Neural Engineering, NER. Vol. 2015-July IEEE Computer Society, 2015. p. 1028-1031 7146802.

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

Taya, F, Sun, Y, Borghini, G, Aricò, P, Babiloni, F, Bezerianos, A & Thakor, NV 2015, Training-induced changes in information transfer efficiency of the brain network: A functional connectome approach. in International IEEE/EMBS Conference on Neural Engineering, NER. vol. 2015-July, 7146802, IEEE Computer Society, pp. 1028-1031, 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015, Montpellier, France, 4/22/15. https://doi.org/10.1109/NER.2015.7146802
Taya F, Sun Y, Borghini G, Aricò P, Babiloni F, Bezerianos A et al. Training-induced changes in information transfer efficiency of the brain network: A functional connectome approach. In International IEEE/EMBS Conference on Neural Engineering, NER. Vol. 2015-July. IEEE Computer Society. 2015. p. 1028-1031. 7146802 https://doi.org/10.1109/NER.2015.7146802
Taya, Fumihiko ; Sun, Yu ; Borghini, Gianluca ; Aricò, Pietro ; Babiloni, Fabio ; Bezerianos, Anastasios ; Thakor, Nitish V. / Training-induced changes in information transfer efficiency of the brain network : A functional connectome approach. International IEEE/EMBS Conference on Neural Engineering, NER. Vol. 2015-July IEEE Computer Society, 2015. pp. 1028-1031
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