Wednesday, May 29, 2013
"Dynamic Network Centrality Summarizes Learning in the Human Brain"
Another of my papers is now out in final form (with a volume, issue number, page numbers, and everything). This article appears in the first issue of Journal of Complex Networks (of which I am an associate editor), so of course the volume number and the issue number are both 1. Naturally, that was still left as a placeholder when the journal posted the article online before the official assignment of that information. :) Additionally, this paper has the largest number of authors of any of my papers. Anyway, here are the details. Title: Dynamic Network Centrality Summarizes Learning in the Human Brain Authors: Alexander V. Mantzaris, Danielle S. Bassett, Nicholas F. Wymbs, Ernesto Estrada, Mason A. Porter, Peter J. Mucha, Scott T. Grafton, and Desmond J. Higham 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 net- work 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.