It is well known that EEG signals of Alzheimer's disease (AD) patients are generally
less synchronous than in age-matched control subjects. However, this effect is not
always easily detectable. This is especially the case for patients in the pre-symptomatic
phase, commonly referred to as mild cognitive impairment (MCI), during which neuronal
degeneration is occurring prior to the clinical symptoms appearance. In this paper,
various synchrony measures are studied in the context of AD diagnosis, including the
correlation coefficient, mean-square and phase coherence, Granger causality, phase
synchrony indices, information-theoretic divergence measures, state space based measures,
and the recently proposed stochastic event synchrony measures. Experiments with EEG
data show that many of those measures are strongly correlated (or anti-correlated)
with the correlation coefficient, and hence, provide little complementary information
about EEG synchrony. Measures that are only weakly correlated with the correlation
coefficient include the phase synchrony indices, Granger causality measures, and stochastic
event synchrony measures. In addition, those three families of synchrony measures
are mutually uncorrelated, and therefore, they each seem to capture a specific kind
of interdependence. For the data set at hand, only two synchrony measures are able
to convincingly distinguish MCI patients from age-matched control patients, i.e.,
Granger causality (in particular, full-frequency directed transfer function) and stochastic
event synchrony. Those two measures are used as features to distinguish MCI patients
from age-matched control subjects, yielding a leave-one-out classification rate of
83%. The classification performance may be further improved by adding complementary
features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic
tool for MCI and AD.