Local Lyapunov exponents detect epileptic zones in spike-less interictal MEG recordings.

OBJECTIVE: In terms of dynamical system theory the rapid alteration of electromagnetic brain signal properties observed with transition from interictal into ictal epileptic activity implies an alteration between at least two dynamical states (attractors). We explored whether such a multistability is reflected also in the dynamical characteristics of the interictal signal. METHODS: A combined method consisting of structural MRI, multichannel magnetoencephalography (MEG) and the non-linear dynamics was applied for the detection of subthreshold interictal activity in temporal lobe epilepsy. Employing multichannel MEG we calculated local Lyapunov exponents (ILE) as a measure of the probability of state transitions in patients with mesiotemporal and extra-mesiotemporal focal epilepsy. RESULTS: The spatial distribution of the variability of ILE was found to be correlated to the side of the epileptic focus, thus, providing a reliable estimate of its localization. CONCLUSION: Our results demonstrate that interictal epileptic activity is characterized by an increase of the number of transitions between different attractors.

Nonlinear mechanisms of cortical oscillations.

Not only theoretical consideration but also analyses of MEG or EEG recordings prove the nonlinear character of cortical dynamics. For instance, an averaged local Lyapunov Exponents (ILE) have positive value that is characteristic for chaotic dynamics. Also a test for nonlinearity (or determinism)--so called surrogate data test distinguishes between original- and randomized-phase time-series proving that recorded signals are nonlinear. These facts are a very strong experimental evidence to support the hypothesis that brain oscillators are governed by the deterministic, nonlinear, low-dimensional dynamics. The experimental manifestations of nonlinear cortical oscillations in the healthy and pathologically altered human brain and their deterministic character seems to be an important step in the understanding brain dynamics in the language of nonlinear systems theory. Clinical application may use nonlinear measures (especially ILE, and PD2i) for classification of pathologies and rough localization of the functional disturbance in the brain.

Deterministic brain oscillations in the magnetoencephalogram.

Determinism is a special property of some systems and is defined by its state-space behavior in which the trajectories in time never intersect. Whether or not determinism exists in brain activities is a question that may be resolved by analysis of the dynamical properties of the electroencephalogram (EEG) or magnetoencephalogram (MEG). We will show that even though there are strong nonstationarities in most brain behaviors, small epochs of deterministic dynamics can still be observed. We will also show that the local Lyapunov exponents are measures that can demonstrate smooth transitions into these deterministic states.

The role of the thalamic reticular neurons in alpha- and gamma-oscillations in neocortex: a mechanism for selective perception and stimulus binding.

The long-term objective is to understand how large masses of neurons in the brain process information during various learning and memory paradigms. Both time- and space-dependent processes have been identified in animals through computer-based analytic quantifications of event-related extracellular potentials. New nonlinear analyses have been introduced that presume that the fine-grain variation in the signal is determined and patterned in phase-space. Some neurons in the primary visual cortex manifest gamma-band oscillations. These cells show both a nonspecific phase-alignment (response synchrony) and a specific tuning (orientation tuning) when stimuli are presented to their receptive fields. This dual regulation of the sensory cells is proposed to underlie stimulus binding, a theoretical mechanism for "object" perception. Nonlinear analytic results from gamma-activities in a simple model neuropil (olfactory bulb) suggest that neuroplasticity may arise through self-organization, a process in which a nonlinear change in the dynamics of the oscillatory field potentials is the hallmark. This self-organization may follow simple dynamical laws in which global cooperativity among the neurons is transiently brought about that, over trials, results in enduring changes in the nonlinear dynamics of some neurons. In conclusion, the sculpturing of the synaptic throughput in the sensory cortex (stimulus binding) may be associated with the irregular phases of the gamma-activities and may result from both specific and nonspecific systems operating together in a nonlinear self-organizing manner.

Dynamical aspects of the EEG in different psychopathological states in an interview situation: a pilot study.

Dynamical brain states can be characterized by non-linear measures of EEG. The present study shows that critical transitions, i.e., abrupt changes from one dynamic pattern of neural mass activity to another one, may be detected by abrupt variations in local chaoticity. Using an ambulatory device, EEG was recorded from 10 patients with a schizophrenic and two patients with an affective disorder during a series of 25-min interviews. Dynamical aspects, in particular, phase transitions in the EEG-dynamics of the EEG were characterized by means of a measure that continuously estimates the chaoticity of the EEG signal and is thus related to its predictability. Results indicate simpler dynamics of the EEG time series in paranoid-hallucinatory patients, while at the same time these patients tended to exhibit more abrupt transitions/unit of time between different dynamical EEG states. Such sudden phase transitions in brain activity were significantly enhanced prior to expressions of thought disorders that were detected by the interviewer and an observer in the conversation, compared with time periods during the interview without such symptoms.

Changes of chaoticness in spontaneous EEG/MEG.

Depending on the task being investigated in EEG/MEG experiments, the corresponding signal is more or less ordered. The question still open is how can one detect the changes of this order while the tasks performed by the brain vary continuously. By applying a static measurement of the fractal dimension or Lyapunov exponent, different brain states could be characterized. However, transitions between different states may not be detected, especially if the moments of transitions are not strictly defined. Here we show how the dynamical measure based on the largest local Lyapunov exponent can be applied for the detection of the changes of the chaoticity of the brain processes measured in EEG and MEG experiments. In this article, we demonstrate an algorithm for computation of chaoticity that is especially useful for nonstationary signals. Moreover, we introduce the idea that chaoticity is able to detect, locally in time, critical jumps (phase-transition-like phenomena) in the human brain, as well as the information flow through the cortex.

Chaos and physiology: deterministic chaos in excitable cell assemblies.

In this review we examined the emerging science of deterministic chaos (nonlinear systems theory) and its application to selected physiological systems. Although many of the popular images of fractals represent fascination and beauty that by analogy corresponds to nature as we see it, the question remains as to its ultimate meaning for physiological processes. It was our intent to help clarify this somewhat popular, somewhat obscure area of nonlinear dynamics in the context of an ever-changing procedural base. We examined not only the basic concepts of chaos, but also its applications ranging from observations in single cells to the complexity of the EEG. We have not suggested that nonlinear dynamics will answer all of our questions; however, we did attempt to illustrate ways in which this approach may help us to answer new questions and to rearticulate old ones. Chaos is revolutionary in that the overall approach requires us to adopt a different frame of reference which, at times, may move us away from previous concerns and methods of data analysis. In sections I-IV, we summarized the nonlinear dynamics approach and described its application to physiology and neural systems. First, we presented a general overview of the application of nonlinear dynamical techniques to neural systems. We discussed the manner in which even apparently simple deterministic systems can behave in an unpredictable manner. Second, we described the principles of nonlinear dynamical systems including the derived analytical techniques. We now see a variety of procedures for delineating whether frenetic chaotic behavior results from a nonlinear dynamical system with a few degrees of freedom, or whether it is caused by an infinite number of variables, i.e., noise. Third, we approached the applications of nonlinear procedures to the cardiovascular systems and to the neurosciences. In terms of time series, we described initial studies which applied the now "traditional" measures of dimensionality (e.g., based on the algorithm by Grassberger and Procaccia) and information change (e.g., Lyapunov exponents). Examples include our own work and that of Pritchard et al., demonstrating that the dynamics of neural mass activity reflect psychopathological states. Today, however, the trend has expanded to include the use of surrogate data and statistical null hypotheses testing to examine whether a given time series can be considered different from that of white or colored noise (cf. Ref. 262). One of the most important potential applications is that of quantifying changes in nonlinear dynamics to predict future states of the system.(ABSTRACT TRUNCATED AT 400 WORDS)

[Evaluation of different methods for detecting resistance of staphylococcus aureus strains to methicillin]. / Ocena wiarygodnosci metod oznaczania opornosci szczepów Staphylococcus aureus na metycyline.

Resistance to methicillin of 70 Staphylococcus aureus isolates from infection and 6 standard strains was evaluated by screening and disc diffusion techniques. Amongst wild S. aureus isolates 28 were identified as methicillin-susceptible (MSSA), 18 as heterogeneously resistant and 24 as homogeneously resistant to methicillin (MRSA). The best detection of methicillin-resistance was obtained by two screening techniques 1) on Mueller-Hinton agar with 6 mg oxacillin per litre and incubation for 40 hours at 37 degrees C. The evaluation of commercial test Crystal MRSA ID System proved its high usefulness in proper detection of MRSA and allowed to get results in 4 hours. Disk diffusion with 1 microgram oxacillin disk and incubation for 20 hours at 30 degrees C prove to be the most reliable out of all disc diffusion techniques studied although inferior to screening techniques.

Tsunamis generated by eruptions from mount st. Augustine volcano, alaska.

During an eruption of the Alaskan volcano Mount St. Augustine in the spring of 1986, there was concern about the possibility that a tsunami might be generated by the collapse of a portion of the volcano into the shallow water of Cook Inlet. A similar edifice collapse of the volcano and ensuing sea wave occurred during an eruption in 1883. Other sea waves resulting in great loss of life and property have been generated by the eruption of coastal volcanos around the world. Although Mount St. Augustine remained intact during this eruptive cycle, a possible recurrence of the 1883 events spurred a numerical simulation of the 1883 sea wave. This simulation, which yielded a forecast of potential wave heights and travel times, was based on a method that could be applied generally to other coastal volcanos.