Reconstruction of the spatio-temporal dynamics of a human magnetoencephalogram

Jirsa, Viktor K.; Friedrich, Rudolf; Haken, H.

doi:10.1016/0167-2789(95)00226-x

Cited by 50 publications

(24 citation statements)

References 8 publications

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“…In fact, in the case of the Kelso experiment four basic patterns can be identified that make up about 97% of the total pattern. Two of the corresponding amplitudes act as order parameters, whereas two further amplitudes are just enslaved by the order parameters (Jirsa et al, 1995). According to the general approach of synergetics the order parameters obey coupled differential equations whose form we derive phenomenologically to match the observed time behavior of the order parameters.…”

Section: Applications Of Synergetics To Brain Functioning Movement Cmentioning

confidence: 99%

Synergetics of brain function

Haken

2006

International Journal of Psychophysiology

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Section: Applications Of Synergetics To Brain Functioning Movement Cmentioning

confidence: 99%

Synergetics of brain function

Haken

2006

International Journal of Psychophysiology

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“…For example, macroscopic variables can be extracted from large-scale observations, such as magnetoencephalography (MEG), using the order parameter concept: order parameters are created and determined by the cooperation of microscopic quantities and yet, at the same time, govern the behaviour of the whole system. We will not deal with these approaches here but interested readers are referred to Jirsa et al (1995) for an example.…”

Section: X(t)ˆf (X(t ¡ U) C)mentioning

confidence: 99%

The labile brain. I. Neuronal transients and nonlinear coupling

Friston

2000

Phil. Trans. R. Soc. Lond. B

273

168

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In this, the ¢rst of three papers, the nature of, and motivation for, neuronal transients is described in relation to characterizing brain dynamics. This paper deals with some basic aspects of neuronal dynamics, interactions, coupling and implicit neuronal codes. The second paper develops neuronal transients and nonlinear coupling in the context of dynamic instability and complexity, and suggests that instability or lability is necessary for adaptive self-organization. The ¢nal paper addresses the role of neuronal transients through information theory and the emergence of spatio-temporal receptive ¢elds and functional specialization.By considering the brain as an ensemble of connected dynamic systems one can show that a su¤cient description of neuronal dynamics comprises neuronal activity at a particular time and its recent history. This history constitutes a neuronal transient. As such, transients represent a fundamental metric of neuronal interactions and, implicitly, a code employed in the functional integration of brain systems. The nature of transients, expressed conjointly in distinct neuronal populations, re£ects the underlying coupling among populations. This coupling may be synchronous (and possibly oscillatory) or asynchronous. A critical distinction between synchronous and asynchronous coupling is that the former is essentially linear and the latter is nonlinear. The nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays a role in functional integration that may be as important as synchronous interactions. The distinction between linear and nonlinear coupling has fundamental implications for the analysis and characterization of neuronal interactions, most of which are predicated on linear (synchronous) coupling (e.g. crosscorrelograms and coherence). Using neuromagnetic data it is shown that nonlinear (asynchronous) coupling is, in fact, more abundant and can be more signi¢cant than synchronous coupling.

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“…This method's well-de®ned mathematical application to time-varying patterns has been established (Newman 1996;Haken 1996), and recently it was applied to spatio-temporal dynamics and low-dimensional chaos (Broomhead and King 1986;Sirovich 1989;Caponeri et al 1992). EEG pattern analysis using KL decomposition (Friedrich et al 1991), the phase transition in the human brain (Jirsa et al 1994), and the synergetic approach (Jirsa et al 1995;Haken 1996) were recently applied to the study of epilepsy. Dvorak (1990) proposed the concept of global dimensional complexity which is composed of multidimensional analogues of the correlation dimension, and it has been shown to be a sensitive measure of drug eects, vigilance, and age (Wackermann et al 1993;Matousek et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Synergetic analysis of spatio-temporal EEG patterns: Alzheimer's disease

Kim

et al. 2001

Biol Cybern

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A spatio-temporal analysis has been employed on EEG signals recorded in groups of patients with Alzheimer's disease (AD). The so-called Karhunen-Loeve analysis method was applied to four groups of subjects: 12 patients who were diagnosed as having severe AD, 8 mild AD patients, 10 vascular dementia patients, and 10 normal aged controls, to obtain the spatio-temporal eigenpatterns. The local difference of the global EEG pattern in mild AD patients from that of normal controls was expanded to the frontal regions in the case of severe AD. The analysis showed significant bilateral temporal functioning differences and inter-hemispheric connection difficulty, along with remarkable intra-hemispheric correlation in severe AD patients. Compared to the normal controls, severe AD patients' brains exhibit only weak local connections and correlations, resulting into limited harmonious inter-hemispheric information processing. The results from the spatio-temporal EEG study of AD patients can be considered to be due to a change in the relative activity of the brain corresponding to the pathologic variation in AD, and the results are in accordance with reported clinical studies.

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Reconstruction of the spatio-temporal dynamics of a human magnetoencephalogram

Cited by 50 publications

References 8 publications

Synergetics of brain function

Synergetics of brain function

The labile brain. I. Neuronal transients and nonlinear coupling

Synergetic analysis of spatio-temporal EEG patterns: Alzheimer's disease

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