Dynamic network centrality summarizes learning in human brain

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Mantzaris, Alexander Vassilios and Bassett, Danielle S. and Wymbs, Nicholas F. and Estrada, Ernesto and Porter, Mason A. and Mucha, Peter J. and Grafton, Scott T. and Higham, Desmond (2013) Dynamic network centrality summarizes learning in human brain. Journal of Complex Networks, 1 (1). pp. 83-92. ISSN 2051-1329 (https://doi.org/10.1093/comnet/cnt001)

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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.

ORCID iDs

Mantzaris, Alexander Vassilios ORCID logoORCID: https://orcid.org/0000-0002-9138-1878, Bassett, Danielle S., Wymbs, Nicholas F., Estrada, Ernesto ORCID logoORCID: https://orcid.org/0000-0002-3066-7418, Porter, Mason A., Mucha, Peter J., Grafton, Scott T. and Higham, Desmond ORCID logoORCID: https://orcid.org/0000-0002-6635-3461;
  • Item type:Article
    ID code:42781
    Dates:
    Date
    Event
    2013
    Published
    26 March 2013
    Published Online
    Notes:changes made
    Subjects:Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:07 Feb 2013 15:52
    Last modified:11 Nov 2024 10:20
    URI:https://strathprints.strath.ac.uk/id/eprint/42781
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