Characterization of EEG Resting-state Activity in Alzheimer's Disease by Means of Recurrence Plot Analyses.

Abstract

The main objective of this study was to characterize EEG resting-state activity in 55 Alzheimer's disease (AD) patients and 29 healthy controls by means of TREND, a measure based on recurrence quantification analysis. TREND was computed from 60-second recordings of consecutive EEG activity, divided into non-overlapping windows of length 1, 2, 3, 5, 10, 15, 20 and 60 seconds. This measure was computed in the conventional EEG frequency bands (delta, theta, alpha, beta-1, beta-2 and gamma). The parameters delay (τ) and embedding dimension (m) were first optimized for every window size and frequency band under study. These embedding parameters proved to be frequency-dependent. Furthermore, 10 s epochs were set as the minimum length required to avoid spurious results. Statistically significant differences between both groups were found (p <; 0.05, Mann-Whitney U-test). The groups showed differences in TREND in the theta (4-8 Hz), beta1 (13-19 Hz) and beta-2 (19-30 Hz) frequency bands. Our results using TREND suggest that AD disrupts resting-state neural dynamics. Furthermore, these findings indicate that AD induces a frequency-dependent pattern of alterations in the non-stationarity levels of resting-state neural activity.