Drug effect on EEG connectivity assessed by linear and nonlinear couplings

Abstract

Quantitative analysis of human electroencephalogram (EEG) is a valuable method for evaluating psychopharmacological agents. Although the effects of different drug classes on EEG spectra are already known, interactions between brain locations remain unclear. In this work, cross mutual information function and appropriate surrogate data were applied to assess linear and nonlinear couplings between EEG signals. The main goal was to evaluate the pharmacological effects of alprazolam on brain connectivity during wakefulness in healthy volunteers using a cross-over, placebo-controlled design. Eighty-five pairs of EEG leads were selected for the analysis, and connectivity was evaluated inside anterior, central, and posterior zones of the scalp. Connectivity between these zones and interhemispheric connectivity were also measured. Results showed that alprazolam induced significant changes in EEG connectivity in terms of information transfer in comparison with placebo. Trends were opposite depending on the statistical characteristics: decreases in linear connectivity and increases in nonlinear couplings. These effects were generally spread over the entire scalp. Linear changes were negatively correlated, and nonlinear changes were positively correlated with drug plasma concentrations; the latter showed higher correlation coefficients. The use of both linear and nonlinear approaches revealed the importance of assessing changes in EEG connectivity as this can provide interesting information about psychopharmacological effects.

Stress assessment based on EEG univariate features and functional connectivity measures

“…CMIF can be calculated using the following equation to quantify the coupling between two signals ξ(t) and η(t) (Alonso et al 2010):…”

Cross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam

“…Neural dynamics and synchronization of brain signals have been increasingly recognized to be an important mechanism to measure coordination of brain regions (Singer 2001;Xie 2010). Synchronous oscillations between different regions may be useful correlates of brain function, and their changes have been used for instance in the study of cognitive processes and depth of anesthesia (Basar et al 2001;Ferenets et al 2006;Miyake et al 2010), drug effects (Fingelkurts et al 2009;Alonso et al 2010;Minc et al 2010), pathologies directly related to cerebral activity (Jeong et al 2001;Stam 2005;Srinivasan et al 2007;Alonso et al 2011), and other diseases (Tong et al 2003;Shin et al 2006). Nonlinear techniques, mainly based on generalized and phase synchronization measures, have lately been used to quantify nonlinear interactions in neurophysiology (David et al 2004;Pereda et al 2005;Stam 2005;Stam et al 2009;Kreuz et al 2007; Montez et al 2009;Wendling et al 2009).…”

The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks

“…13 The equiquantal mutual information estimator has been successfully used in detection and quantification of nonlinearity in the dynamics of various complex systems, ranging from the Earth's atmosphere 14 to the human heart 15 and brain. [16][17][18][19] In neuroscience, functional magnetic resonance imaging (fMRI) is one of the prominent methods for the study of large-scale brain dynamics. Following the increase of interest in the study of complex network properties, a wealth of graph-theoretical studies utilizing resting-state fMRI data has been published in the last 5 yr. 20 Importantly, linear connectivity measures such as Pearson or partial linear correlation of the local activity time series are used to derive the connectivity matrix that is then transformed to the network representation.…”