Dynamic network centrality summarizes learning in human brain

Alexander Vassilios Mantzaris, Danielle S. Bassett, Nicholas F. Wymbs, Ernesto Estrada, Mason A. Porter, Peter J. Mucha, Scott T. Grafton, Desmond Higham

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces signicant evidence of `learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time progresses. However, the high dimensionality and time-dependent nature of the data makes it dicult to explain which brain regions are driving this distinction. Using network centrality
measures that respect the arrow of time, we express the data in an extremely compact form that characterizes the aggregate activity of each brain region in each experiment using a single coecient, while reproducing information about learning that was discovered using the full data set. This compact summary allows key brain regions con-
tributing to centrality to be visualized and interpreted. We thereby provide a proof of principle for the use of recently proposed dynamic centrality measures on temporal network data in neuroscience.
LanguageEnglish
Pages83-92
Number of pages10
JournalJournal of Complex Networks
Volume1
Issue number1
Early online date26 Mar 2013
DOIs
Publication statusPublished - 2013

Fingerprint

Centrality
Dynamic Networks
Brain
Unsupervised Clustering
Functional Magnetic Resonance Imaging
Neuroscience
Dimensionality
Express
Human
Learning
Network centrality
Experiments
Experiment

Keywords

  • dynamic network centrality
  • learning
  • human brain
  • functional activity
  • magnetic resonance imaging

Cite this

Mantzaris, A. V., Bassett, D. S., Wymbs, N. F., Estrada, E., Porter, M. A., Mucha, P. J., ... Higham, D. (2013). Dynamic network centrality summarizes learning in human brain. Journal of Complex Networks, 1(1), 83-92. https://doi.org/10.1093/comnet/cnt001
Mantzaris, Alexander Vassilios ; Bassett, Danielle S. ; Wymbs, Nicholas F. ; Estrada, Ernesto ; Porter, Mason A. ; Mucha, Peter J. ; Grafton, Scott T. ; Higham, Desmond. / Dynamic network centrality summarizes learning in human brain. In: Journal of Complex Networks. 2013 ; Vol. 1, No. 1. pp. 83-92.
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Mantzaris, AV, Bassett, DS, Wymbs, NF, Estrada, E, Porter, MA, Mucha, PJ, Grafton, ST & Higham, D 2013, 'Dynamic network centrality summarizes learning in human brain' Journal of Complex Networks, vol. 1, no. 1, pp. 83-92. https://doi.org/10.1093/comnet/cnt001

Dynamic network centrality summarizes learning in human brain. / Mantzaris, Alexander Vassilios; Bassett, Danielle S.; Wymbs, Nicholas F.; Estrada, Ernesto; Porter, Mason A.; Mucha, Peter J.; Grafton, Scott T.; Higham, Desmond.

In: Journal of Complex Networks, Vol. 1, No. 1, 2013, p. 83-92.

Research output: Contribution to journalArticle

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Mantzaris AV, Bassett DS, Wymbs NF, Estrada E, Porter MA, Mucha PJ et al. Dynamic network centrality summarizes learning in human brain. Journal of Complex Networks. 2013;1(1):83-92. https://doi.org/10.1093/comnet/cnt001