Nonlinear synchronization in EEG and whole‐head MEG recordings of healthy subjects

Abstract

According to Friston, brain dynamics can be modelled as a large ensemble of coupled nonlinear dynamical subsystems with unstable and transient dynamics. In the present study, two predictions from this model (the existence of nonlinear synchronization between macroscopic field potentials and itinerant nonlinear dynamics) were investigated. The dependence of nonlinearity on the method of measuring brain activity (EEG vs. MEG) was also investigated. Dataset I consisted of 10 MEG recordings in 10 healthy subjects. Dataset II consisted of simultaneously recorded MEG (126 channels) and EEG (19 channels) in 5 healthy subjects. Nonlinear coupling was assessed with the synchronization likelihood S and dynamic itinerancy with the synchronization entropy Hs. Significance was assessed with a bootstrap procedure ("surrogate data testing"), comparing S and Hs with their distribution under the null hypothesis of stationary, linear dynamics. Significant nonlinear synchronization was detected in 14 of 15 subjects. The nonlinear dynamics were associated with a high index of itinerant behaviour. Nonlinear interdependence was significantly more apparent in MEG data than EEG. Synchronous oscillations in MEG and EEG recordings contain a significant nonlinear component that exhibits characteristics of unstable and itinerant behaviour. These findings are in line with Friston's proposal that the brain can be conceived as a large ensemble of coupled nonlinear dynamical subsystems with labile and unstable dynamics. The spatial scale and physical properties of MEG acquisition may increase the sensitivity of the data to underlying nonlinear structure.

Natural world physical, brain operational, and mind phenomenal space–time

“…Furthermore, several such assemblies in one cortical area are synchronized with a set of local assemblies in distant brain areas [67,121,122]. Stam [123] expresses it in the most explicit way: "Neurophysiology has become neuron-physiology, and later molecular biology of the neuron.…”

EEG Power Asymmetry and Functional Connectivity as a Marker of Treatment Effectiveness in DBS Surgery for Depression

“…Furthermore, these studies have used different approaches and methodologies to investigate differences in brain dynamics between depressed patients and healthy controls making it difficult to compare outcomes. For example, Lee et al (2011) used correlations between power series of channel pairs as a measure of connectivity; Leistedt et al (2009) used synchronization likelihood (Stam et al, 2003); whereas Fingelkurts et al (2007) used an in-house synchronization measure termed index of structural synchronization (Fingelkurts and Kahkonen, 2005). Although these and other EEG/MEG measures of connectivity (phase coherency, phase lag index, imaginary coherency, etc) have been shown to capture aspects of correlations/synchronization between two time series, they are known to perform differently.…”

Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude

“…Long axons of excitatory neurons with high conduction velocities support synchronization over large areas of cortex (Freeman, 2003b), creating small-world effects (Watts and Strogatz, 1998;Wang and Chen, 2003) in analogy to the rapid dissemination of information through social contacts. The importance of long-distance correlations has been emphasized by numerous brain theorists (e.g., Ingber, 1995;Hoppenstaedt and Izhkevich, 1998;Haken, 1999;Friston, 2000;LinkenkaerHansen et al, 2001;Vitiello, 2001;Kaneko and Tsuda, 2001;Kozma, Freeman and Erdí, 2003;Stam et al, 2003).…”