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J Neural Transm (Vienna). 2016 Mar;123(3):297-316. doi: 10.1007/s00702-015-1461-x. Epub 2015 Sep 28.

Quantifying synchrony patterns in the EEG of Alzheimer's patients with linear and non-linear connectivity markers.

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AIT Austrian Institute of Technology GmbH, Vienna, Austria.
AIT Austrian Institute of Technology GmbH, Vienna, Austria.
Department of Neurology, Clinical Section of Neurogeriatrics, Graz Medical University, Graz, Austria.
Department of Neurology, Innsbruck Medical University, Innsbruck, Austria.
Department of Neurology, Vienna Medical University, Vienna, Austria.
Department of Neurology and Psychiatry, Linz General Hospital, Linz, Austria.
Institute of Molecular Biology and Biochemistry, Graz Medical University, Graz, Austria.
Dr. Grossegger and Drbal GmbH, Vienna, Austria.
Institute for Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria.


We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.


Alzheimer’s disease; Canonical correlation; Coherence; Compensatory mechanism; EEG synchrony markers; Granger causality

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