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Brain Advance Access published online on October 24, 2008
Brain, doi:10.1093/brain/awn262
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© The Author (2008). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease

C. J. Stam1, W. de Haan2, A. Daffertshofer3, B. F. Jones4, I. Manshanden1, A. M. van Cappellen van Walsum5,6, T. Montez7, J. P. A. Verbunt1,8, J. C. de Munck8, B. W. van Dijk1,8, H. W. Berendse2 and P. Scheltens2

1Department of Clinical Neurophysiology and MEG, 2Department of Neurology, Alzheimer Center, VU University Medical Center, Amsterdam, 3Research Institute MOVE, VU University, Van der Boechorststraat 9, 1081 BT Amsterdam, the Netherlands, 4Dementia Research Centre, Institute of Neurology, UCL, London, UK, 5Department of Anatomy, Radboud University Nijmegen Medical Centre, Nijmegen, 6Institute of Technical Medicine, University of Twente, Enschede, the Netherlands, 7Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, Portugal and 8Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, the Netherlands

Correspondence to: Willem de Haan, Department of Neurology, Alzheimer Center, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, the Netherlands E-mail: w.dehaan{at}vumc.nl

In this study we examined changes in the large-scale structure of resting-state brain networks in patients with Alzheimer's disease compared with non-demented controls, using concepts from graph theory. Magneto-encephalograms (MEG) were recorded in 18 Alzheimer's disease patients and 18 non-demented control subjects in a no-task, eyes-closed condition. For the main frequency bands, synchronization between all pairs of MEG channels was assessed using a phase lag index (PLI, a synchronization measure insensitive to volume conduction). PLI-weighted connectivity networks were calculated, and characterized by a mean clustering coefficient and path length. Alzheimer's disease patients showed a decrease of mean PLI in the lower alpha and beta band. In the lower alpha band, the clustering coefficient and path length were both decreased in Alzheimer's disease patients. Network changes in the lower alpha band were better explained by a ‘Targeted Attack’ model than by a ‘Random Failure’ model. Thus, Alzheimer's disease patients display a loss of resting-state functional connectivity in lower alpha and beta bands even when a measure insensitive to volume conduction effects is used. Moreover, the large-scale structure of lower alpha band functional networks in Alzheimer's disease is more random. The modelling results suggest that highly connected neural network ‘hubs’ may be especially at risk in Alzheimer's disease.

Key Words: Alzheimer's disease; functional connectivity; MEG; synchronization; small-world networks

Abbreviations: EEG, electro-encephalography; MEG, Magneto-encephalography; MMSE, mini mental state examination; PLI, phase lag index; SL, synchronization likelihood

Received May 5, 2008. Revised September 12, 2008. Accepted September 18, 2008.


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