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Unformatted text preview: Small-World Networks and Functional Connectivity in Alzheimers Disease C. J. Stam 1,2 , B. F. Jones 2 , G. Nolte 3,4 , M. Breakspear 5,6 and Ph. Scheltens 2 1 Department of Clinical Neurophysiology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands, 2 Alzheimer Center, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands, 3 Human Motor Control Section, NINDS, National Institutes of Health, 10 Center Drive MSC 1428 Bethesda, MD, USA, 4 Fraunhofer First, Kekulestrae 7, 12489 Berlin, Germany, 5 Brain Dynamics Centre, Westmead Hospital, Westmead, New South Wales 2145, Australia and 6 School of Physics, University of Sydney, Sydney, Australia Thefirstandsecondauthorshavecontributedequallytothiswork We investigated whether functional brain networks are abnormally organized in Alzheimers disease (AD). To this end, graph theoret- ical analysis was applied to matrices of functional connectivity of beta band--filtered electroencephalography (EEG) channels, in 15 Alzheimer patients and 13 control subjects. Correlations between all pairwise combinations of EEG channels were determined with the synchronization likelihood. The resulting synchronization matri- ces were converted to graphs by applying a threshold, and cluster coefficients and path lengths were computed as a function of threshold or as a function of degree K . For a wide range of thresh- olds, the characteristic path length L was significantly longer in the Alzheimer patients, whereas the cluster coefficient C showed no significant changes. This pattern was still present when L and C were computed as a function of K . A longer path length with a relatively preserved cluster coefficient suggests a loss of complex- ity and a less optimal organization. The present study provides further support for the presence of small-world features in func- tional brain networks and demonstrates that AD is characterized by a loss of small-world network characteristics. Graph theoretical analysis may be a useful approach to study the complexity of patterns of interrelations between EEG channels. Keywords: Alzheimers disease, complexity, EEG, small-world network, synchronization Introduction According to Delbeuck and others (2003), cognitive dysfunc- tion in Alzheimers disease (AD) could be due, at least in part, to a functional disconnection between distant brain areas. One possibility to examine this hypothesis is to study the correla- tions between signals of brain activity (EEG, magnetoencepha- lography [MEG] functional magnetic resonance imaging [fMRI] blood oxygen level dependent [BOLD]) recorded from different areas. The underlying assumption is that such correlations reect, at least in part, functional interactions between different brain areas. The concept of statistical interdependencies be- tween signals of brain activity as a tentative index of functional interactions is referred to as functional connectivity (for a...
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This note was uploaded on 02/17/2011 for the course HK 490 taught by Professor Reidtky during the Fall '10 term at Purdue University-West Lafayette.
- Fall '10