Skip Navigation

Small-World Networks and Functional Connectivity in Alzheimer's Disease

  1. Ph Scheltens2

+ Author Affiliations

  1. 1Department of Clinical Neurophysiology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands
  2. 2Alzheimer Center, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands
  3. 3Human Motor Control Section, NINDS, National Institutes of Health, 10 Center Drive MSC 1428 Bethesda, MD, USA
  4. 4Fraunhofer First, Kekulestrae 7, 12489 Berlin, Germany
  5. 5Brain Dynamics Centre, Westmead Hospital, Westmead, New South Wales 2145, Australia
  6. 6School of Physics, University of Sydney, Sydney, Australia
  1. Address correspondence to B. F. Jones, Department of Neurology, VU University Medical Centre, PO Box 7057, 1007 MB Amsterdam, The Netherlands. Email: b.jones@vumc.nl.

    Abstract

    We investigated whether functional brain networks are abnormally organized in Alzheimer's disease (AD). To this end, graph theoretical 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 matrices 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 thresholds, 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 complexity and a less optimal organization. The present study provides further support for the presence of “small-world” features in functional 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.

    Key words

      Articles citing this article

      | Table of Contents

      Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.