The Mathematical Institute, University of Oxford, Eprints Archive

Dynamic network centrality summarizes learning in the human brain

Mantzaris, A V and Bassett, D S and Wymbs, N F and Estrada, E and Porter, M A and Mucha, P J and Grafton, S T (2013) Dynamic network centrality summarizes learning in the human brain. Journal of Complex Networks, 1 (1). pp. 83-92.

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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 3 days of practice produces significant 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 difficult 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 coefficient, while reproducing information about learning that was discovered using the full data set. This compact summary allows key brain regions contributing 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.

Item Type:Article
Uncontrolled Keywords:dynamic walks functional magnetic resonance imaging fMRI motor task dynamic centrality matrix resolvent temporal network brain networks
Subjects:A - C > Biology and other natural sciences
Research Groups:Centre for Mathematical Biology
ID Code:1801
Deposited By: Sara Jolliffe
Deposited On:21 Feb 2014 08:47
Last Modified:29 May 2015 19:29

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