Biomedical subjects

Michael Breakspear

Publications and source records attributed to Michael Breakspear.

4 recordsLinked to original sources

PubMed2003

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

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.

Adult↗

PubMed2003

An improved algorithm for the detection of dynamical interdependence in bivariate time-series.

We describe a new algorithm for the detection of dynamical interdependence in bivariate time-series data sets. By using geometrical and dynamical arguments, we produce a method that can detect dynamical interdependence in weakly coupled systems where previous techniques have failed. We illustrate this by comparison of our algorithm with another commonly used technique when applied to a system of coupled Hénon maps. In addition, an improvement of approximately 20% in the detection rate is observed when the technique is applied to human scalp EEG data, as compared with existing techniques. Such an improvement may assist an understanding of the role of large-scale nonlinear processes in normal brain function.

Algorithms↗

PubMed2003

Synchronous gamma activity: a review and contribution to an integrative neuroscience model of schizophrenia.

Synchronous high frequency (Gamma band) activity has been proposed as a candidate mechanism for the integration or 'binding' of distributed brain activities. Since the first descriptions of schizophrenia, attempts to characterize this disorder have focused on disturbances in such integrative processing. Here, we review both micro- and macroscopic neuroscience research into Gamma synchrony, and its application to understanding schizophrenia. The review encompasses evidence from both animal and human studies for the functional significance of Gamma activity, the association between Gamma dysfunction and information processing disturbances, and the relevance of specific Gamma dysfunctions to the integration and extension of previous disconnection models of schizophrenia. Attention is given to the relationship between Gamma activity and the heterogeneous symptoms of schizophrenia. Existing studies show that measures of Gamma activity have the potential to explain far more of the variance in schizophrenia performance than previous neurophysiological measures. It is concluded that measures of Gamma synchrony offer a valuable window into the core integrative disturbance in schizophrenia cognition.

Action Potentials↗

PubMed2002

Nonlinear phase desynchronization in human electroencephalographic data.

Ensembles of coupled nonlinear systems represent natural candidates for the modeling of brain dynamics. The objective of this study is to examine the complex signal produced by coupled chaotic attractors, to discuss their potential relevance to distributed processes in the brain, and to illustrate a method of detecting their contribution to human EEG morphology. Two measures of quantifying the behavior of coupled nonlinear systems are presented: a measure of phase synchrony and a novel measure of intermittent phase desynchronization. These are used to quantify the behavior of numerical examples of coupled chaotic attractors. Experimental evidence of their contribution to the morphology of the human alpha rhythm is then illustrated in a study of EEG recordings from 40 healthy human subjects. Amplitude-adjusted phase-randomized surrogate data is used to test the null hypothesis that the observed patterns of phase coherence can be described by purely linear methods. Statistical analysis reveals that this null hypothesis can be robustly rejected in a small number (approximately 4%) of EEG epochs. These findings are discussed with reference to the adaptive function and complex dynamics of the brain.

Adult↗