ABSTRACT
Detrended fluctuation analysis (DFA) is widely used to characterize long-range power-law correlations in complex signals. However, it has restrictions when nonstationarity is not limited only to slow variations in the mean value. To improve the characterization of inhomogeneous datasets, we have proposed the extended DFA (EDFA), which is a modification of the conventional method that evaluates an additional scaling exponent to take into account the features of time-varying nonstationary behavior. Based on EDFA, here, we analyze rat electroencephalograms to identify specific changes in the slow-wave dynamics of brain electrical activity associated with two different conditions, such as the opening of the blood-brain barrier and sleep, which are both characterized by the activation of the brain drainage function. We show that these conditions cause a similar reduction in the scaling exponents of EDFA. Such a similarity may represent an informative marker of fluid homeostasis of the central nervous system.
Subject(s)
Blood-Brain Barrier , Electroencephalography , Animals , Rats , SleepABSTRACT
We study the synchronization of infra-slow oscillations in human scalp electroencephalogram signal with the respiratory signal. For the cases of paced respiration with a fixed frequency and linearly increasing frequency, we reveal the phase and frequency locking of infra-slow oscillations of brain potentials by respiration. It is shown that for different brain areas, the infra-slow oscillations and respiration can exhibit synchronous regimes of different orders.
Subject(s)
Biological Clocks/physiology , Brain Waves/physiology , Models, Neurological , Respiration , Humans , Scalp/physiologyABSTRACT
We investigate the effects of a linear resonator on the high-frequency dynamics of electrons in devices exhibiting negative differential conductance. We show that the resonator strongly affects both the dc and ac transport characteristics of the device, inducing quasiperiodic and high-frequency chaotic current oscillations. The theoretical findings are confirmed by experimental measurements of a GaAs/AlAs miniband semiconductor superlattice coupled to a linear microstrip resonator. Our results are applicable to other active solid state devices and provide a generic approach for developing modern chaos-based high-frequency technologies including broadband chaotic wireless communication and superfast random-number generation.
ABSTRACT
It is known that sleep spindles are produced by thalamo-cortical system spontaneously during the slow-wave sleep; pathological processes in thalamo-cortical network might cause absence epilepsy. The aim of this study was to examine age-dependent changes in time-frequency structure of sleep spindles in parallel to a progressive increase in amount of absence seizures in WAG/Rij rat model. EEG was consistently recorded at the age of 5, 7 and 9 months by means of epidural electrodes implanted in the frontal cortex. Continuous wavelet transform was used for automatic identification and further time-frequency analysis of sleep spindles in EEG. It was found that the mean duration of epileptic discharges and total duration of epileptic activity increased with age, whereas the length of sleep spindles decreased. Mean frequency of oscillations within a spindle was used as a criterion for dividing sleep spindles in three categories: "slow" (9.3 Hz), "tr ansitional" (11.4 Hz) and "fast" (13.5 Hz). "Slow" and "transitional" spindles in 5-months animals displayed an increase in frequency from the beginning towards the end. It was shown that the higher incidence of epilepsy corresponded to the lower duration of sleep spindles (all types). Mean frequency of "transitional" and "fast" spindles was higher in rats with more intensive epileptic discharges. In general, high epileptic activity in WAG/Rij rats corresponded to the most substantial changes within "transitional" spindles, whereas changes within slow and fast spindles were moderate.
