Picture of boy being examining by doctor at a tuberculosis sanatorium

Understanding our future through Open Access research about our past...

Strathprints makes available scholarly Open Access content by researchers in the Centre for the Social History of Health & Healthcare (CSHHH), based within the School of Humanities, and considered Scotland's leading centre for the history of health and medicine.

Research at CSHHH explores the modern world since 1800 in locations as diverse as the UK, Asia, Africa, North America, and Europe. Areas of specialism include contraception and sexuality; family health and medical services; occupational health and medicine; disability; the history of psychiatry; conflict and warfare; and, drugs, pharmaceuticals and intoxicants.

Explore the Open Access research of the Centre for the Social History of Health and Healthcare. Or explore all of Strathclyde's Open Access research...

Image: Heart of England NHS Foundation Trust. Wellcome Collection - CC-BY.

Dynamic network centrality summarizes learning in human brain

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

[img] PDF
mantz_final.pdf
Preprint
License: Creative Commons Attribution-NonCommercial 4.0 logo

Download (3MB)

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.