This study shows that the arterial longitudinal impedance constitutes a hemodynamic parameter of interest for performance characterization of large arteries in normal condition as well as in pathological situations. For this purpose, we solved the Navier–Stokes equations for an incompressible flow using the finite element analysis method and the Arbitrary Lagrangian Eulerian (ALE) formulation. The mathematical model assumes a two-dimensional flow and takes into account the nonlinear terms in the equations of fluid motion that express the convective acceleration, as well as the nonlinear deformation of the arterial wall. Several numerical simulations of the blood flow in large vessels have been performed to study the propagation along an arterial vessel of a pressure gradient pulse and a rate flow pulse. These simulations include various deformations of the wall artery leading to parietal displacements ranging from 0 (rigid wall) to 15% (very elastic wall) in order to consider physiological and pathological cases.
The results show significant changes of the rate flow and the pressure gradient wave as a function of aosc, the relative variation in the radius of the artery over a cardiac cycle. These changes are notable beyond a critical value of aosc equal to 0.05. This critical value is also found in the evolution of the longitudinal impedance. So, above a variation of radius of 5%, the convective acceleration, created by the fluid-wall interactions, have an influence on the flow detectable on the longitudinal impedance.
The interpretation of the evolution of the longitudinal impedance shows that it could be a mean to test the performance of large arteries and can contribute to the diagnosis of parietal lesions of large arteries. For a blood vessel with a wall displacement higher than 5% similar to those of large arteries like the aorta, the longitudinal impedance is substantially greater than that obtained in the absence of wall displacement. This study also explains the effects of convective acceleration, on the shape of the decline of the pressure gradient wave and shows that they should not be neglected when the variation in radius is greater than 5%.
In this paper, we introduce fractional-order into a model of HIV-1 infection of CD4+ T cells. We study the effect of the changing the average number of viral particles N with different sets of initial conditions on the dynamics of the presented model. Generalized Euler method (GEM) will be used to find a numerical solution of the HIV-1 infection fractional order model.
The effect of exogenous, highly diluted formaldehyde on the rate of demethylation/re-methylation of veratric acid by the bacteria Rhodococcus erythropolis was studied using electrophoretic and microscopic techniques. The activity of 4-O-demethylase, responsible for accumulation of vanillic acid, and the levels of veratric and vanillic acids were determined using capillary electrophoresis. Formaldehyde was serially diluted at 1:100 ratios, and the total number of iterations was 20. After incubation of the successive dilutions of formaldehyde with the bacteria, demethylase activity oscillated in a sinusoidal manner. It was established using capillary electrophoresis that methylation of vanillic acid to veratric acid occurred at a double rate, as shown by the doubled fluctuation in the concentration of veratrate. There were also changes in the NADH oxidase activity, which is associated with methylation processes. Microscopic observations revealed the presence of numerous enlarged vacuoles in bacterial cells during the accumulation of large amounts of vanillic acid, and their disappearance together with a decrease in 4-O-demethylase activity. The presented results give evidence for the ability of living cells to detect the presence of submolecular concentrations of biological effectors in their environment and provide a basis for a scientific explanation of the law of hormesis and the therapeutic effect of homeopathic dilutions.
formaldehyde; O-demethylases; homeopathy; Rhodococcus erythropolis; low doses
We introduce a novel approach of stabilizing the dynamics of excitation waves by spatially extended sub-threshold periodic forcing. Entrainment of unstable primary waves has been studied numerically for different amplitudes and frequencies of additional sub-threshold stimuli. We determined entrainment regimes under which excitation blocks were transformed into consistent 1:1 responses. These responses were spatially homogeneous and synchronized in the entire excitable medium. Compared to primary pulses, pulses entrained by secondary stimulations were stable at considerably shorter periods which decreased at higher amplitudes and greater number of secondary stimuli. Our results suggest a practical methodology for stabilization of excitation in reaction-diffusion media such as nerve tissue with regions of reduced excitability.
We propose several models applicable to both selection and election processes when each selecting or electing subject has access to different information about the objects to choose from. We wrote special software to simulate these processes. We consider both the cases when the environment is neutral (natural process) as well as when the environment is involved (controlled process).
econophysics, preferences, impact, selection process, mating, monogamic, polygamic, election campaign, spin, influence of environment
In this paper we present a multi-strain model for hepatitis C virus (HCV) including an immune response term. The model is presented and discussed. Also we argue that the added multi-strain term represents some basic properties of the immune system and that it should be included to study longer term behavior of the disease.
There are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. However, most of the studies are limited to group differences in children. Independent component analysis (ICA) separates a set of mixed event-related potentials into a corresponding set of statistically independent source signals, which are likely to represent different functional processes. Using a support vector machine (SVM), a classification method originating from machine learning, this study aimed at investigating the use of such independent ERP components in differentiating adult ADHD patients from non-clinical controls by selecting a most informative feature set. A second aim was to validate the predictive power of the SVM classifier by means of an independent ADHD sample recruited at a different laboratory.
Two groups of age-matched adults (75 ADHD, 75 controls) performed a visual two stimulus go/no-go task. ERP responses were decomposed into independent components, and a selected set of independent ERP component features was used for SVM classification.
Using a 10-fold cross-validation approach, classification accuracy was 91%. Predictive power of the SVM classifier was verified on the basis of the independent ADHD sample (17 ADHD patients), resulting in a classification accuracy of 94%. The latency and amplitude measures which in combination differentiated best between ADHD patients and non-clinical subjects primarily originated from independent components associated with inhibitory and other executive operations.
This study shows that ERPs can substantially contribute to the diagnosis of ADHD when combined with up-to-date methods.
Changes caused by clonidine in rodent electroencephalograms (EEG) have been reported with some inconsistency. For this reason, a pre-clinical study was conducted in order to confirm previous findings with both a standard spectral analysis and a sleep stage scoring procedure. In addition, a nonlinear technique for analysing the time-varying signals was implemented to compare its performance against conventional approaches.
The nonlinear method succeeds in quantifying all dose-related responses from the data set relying solely on the EEG trace.
Nonlinear approaches can deliver a suitable alternative to the sleep-stage scoring methods commonly used for drug effect detection.
Analysis of signals by means of symbolic methods consists in calculating a measure of signal complexity, for example informational entropy or Lempel-Ziv algorithmic complexity. For construction of these entropic measures one uses distributions of symbols representing the analyzed signal.
We introduce a new signal characteristic named sequential spectrum that is suitable for analysis of the wide group of signals, including biosignals.
The paper contains a brief review of analyses of artificial signals showing features similar to those of biosignals. An example of using sequential spectrum for analyzing EEG signals registered during different stages of sleep is also presented.
Sequential spectrum is an effective tool for general description of nonstationary signals and it its advantage over Fourier spectrum. Sequential spectrum enables assessment of pathological changes in EEG-signals recorded in persons with epilepsy.
Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized.
To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.
We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort to simulate large scale networks. For this reason we considered a reduced order model, the Izhikevich one, which is computationally much lighter. The comparison between neural lattices built using both neuron models provided comparable results, both without traditional stimulation and in presence of all the stimulation protocols. This was a first result toward the study and simulation of the large scale neural networks involved in pathological dynamics.
Using the reduced order model an inspection on the activity of two neural lattices was also carried out at the aim to analyze how the stimulation in one area could affect the dynamics in another area, like the usual medical treatment protocols require.
The study of population dynamics that was carried out allowed us to investigate, through simulations, the positive effects of the stimulation signals in terms of desynchronization of the neural dynamics.
The results obtained constitute a significant added value to the analysis of synchronization and desynchronization effects due to neural stimulation. This work gives the opportunity to more efficiently study the effect of stimulation in large scale yet computationally efficient neural networks. Results were compared both with the other mathematical models, using Morris Lecar and Izhikevich neurons, and with simulated Local Field Potentials (LFP).
Markers of temporal changes in central blood volume are required to non-invasively detect hemorrhage and the onset of hemorrhagic shock. Recent work suggests that pulse pressure may be such a marker. A new approach to tracking blood pressure, and pulse pressure specifically is presented that is based on a new form of pulse pressure wave analysis called Pulse Decomposition Analysis (PDA). The premise of the PDA model is that the peripheral arterial pressure pulse is a superposition of five individual component pressure pulses, the first of which is due to the left ventricular ejection from the heart while the remaining component pressure pulses are reflections and re-reflections that originate from only two reflection sites within the central arteries. The hypothesis examined here is that the PDA parameter T13, the timing delay between the first and third component pulses, correlates with pulse pressure.
T13 was monitored along with blood pressure, as determined by an automatic cuff and another continuous blood pressure monitor, during the course of lower body negative pressure (LBNP) sessions involving four stages, -15 mmHg, -30 mmHg, -45 mmHg, and -60 mmHg, in fifteen subjects (average age: 24.4 years, SD: 3.0 years; average height: 168.6 cm, SD: 8.0 cm; average weight: 64.0 kg, SD: 9.1 kg).
Statistically significant correlations between T13 and pulse pressure as well as the ability of T13 to resolve the effects of different LBNP stages were established. Experimental T13 values were compared with predictions of the PDA model. These interventions resulted in pulse pressure changes of up to 7.8 mmHg (SE = 3.49 mmHg) as determined by the automatic cuff. Corresponding changes in T13 were a shortening by -72 milliseconds (SE = 4.17 milliseconds). In contrast to the other two methodologies, T13 was able to resolve the effects of the two least negative pressure stages with significance set at p < 0.01.
The agreement of observations and measurements provides a preliminary validation of the PDA model regarding the origin of the arterial pressure pulse reflections. The proposed physical picture of the PDA model is attractive because it identifies the contributions of distinct reflecting arterial tree components to the peripheral pressure pulse envelope. Since the importance of arterial pressure reflections to cardiovascular health is well known, the PDA pulse analysis could provide, beyond the tracking of blood pressure, an assessment tool of those reflections as well as the health of the sites that give rise to them.
We propose new method of assessment of histological images for medical diagnostics. 2-D image is preprocessed to form 1-D landscapes or 1-D signature of the image contour and then their complexity is analyzed using Higuchi's fractal dimension method. The method may have broad medical application, from choosing implant materials to differentiation between benign masses and malignant breast tumors.
In this paper we consider the fractional order model with two immune effectors interacting with two strain antigen. The systems may explain the recurrence of some diseases e.g. tuberculosis (TB). The stability of equilibrium points are studied. Numerical solutions of this model are given. Using integer order system the system oscillates. Using fractional order system the system converges to a stable internal equilibrium. Ulam-Hyers stability of the system has been studied.
The human menstrual cycle is known to exhibit a significant amount of unexplained variability. This variation is typically dismissed as random fluctuations in an otherwise periodic and predictable system. Given the many delayed nonlinear feedbacks in the multiple levels of the reproductive endocrine system, however, the menstrual cycle can properly be construed as the output of a nonlinear dynamical system, and such a system has the possibility of being in a chaotic trajectory. We hypothesize that this is in fact the case and that it accounts for the observed variability.
Here, we test this hypothesis by performing time series analyses on data for 7749 menstrual cycles from 40 women in the 20-40 year age range, using the database maintained by the Tremin Research Program on Women's Health. Both raw menstrual cycle length data and a formal time series constructed from this data are utilized in these analyses. Employing phase space reconstruction techniques with a maximum embedding dimension of 12, we find appropriate scaling behavior in the correlation sums for these data, indicating low dimensional deterministic dynamics. A correlation dimension of Dc ≈ 5.2 is measured in the scaling regime. This result is confirmed by recalculation using the Takens estimator and by surrogate data tests. We interpret this result as an approximation to the fractal dimension of a strange attractor governing chaotic dynamics in the menstrual cycle. We also use the time series to calculate the correlation entropy (K2 ≈ 0.008/τ) and the maximal Lyapunov exponent (λ ≈ 0.005/τ) for the system, where τ is the sampling time of the series.
Taken collectively, these results constitute significant evidence that the menstrual cycle is the result of chaos in a nonlinear dynamical system. This view of the menstrual cycle has potential implications for clinical practice, modelling of the endocrine system, and the interpretation of the perimenopausal transition.
Autistic spectrum disorders are a group of neurological and developmental disorders associated with social, communication, sensory, behavioral and cognitive impairments, as well as restricted, repetitive patterns of behavior, activities, or interests.
The aim of this study was a) to analyze QEEG findings of autistic patients and to compare the results with data base; and b) to introduce the calculation of spectrum weighted frequency (brain rate) as an indicator of general mental arousal in these patients.
Results for Q-EEG shows generally increased delta-theta activity in frontal region of the brain. Changes in QEEG pattern appeared to be in a non-linear correlation with maturational processes.
Brain rate measured in CZ shows slow brain activity (5. 86) which is significantly lower than normal and corresponds to low general mental arousal.
Recent research has shown that autistic disorders have as their basis disturbances of neural connectivity. Neurofeedback seems capable of remediating such disturbances when these data are considered as part of treatment planning.
Prognosis of this pervasive disorder depends on the intellectual abilities: the better intellectual functioning, the possibilities for life adaptation are higher
QEEG shows generally increased delta-theta activity in frontal region of the brain which is related to poor cognitive abilities.
Brain rate measured in CZ shows slow brain activity related to under arousal.
Pharmacotherapy combined with behavior therapy, social support and especially neurofeedback technique promise slight improvements
In the context of sensory and cognitive-processing deficits in ADHD patients, there is considerable evidence of altered event related potentials (ERP). Most of the studies, however, were done on ADHD children. Using the independent component analysis (ICA) method, ERPs can be decomposed into functionally different components. Using the classification method of support vector machine, this study investigated whether features of independent ERP components can be used for discrimination of ADHD adults from healthy subjects.
Two groups of age- and sex-matched adults (74 ADHD, 74 controls) performed a visual two stimulus GO/NOGO task. ERP responses were decomposed into independent components by means of ICA. A feature selection algorithm defined a set of independent component features which was entered into a support vector machine.
The feature set consisted of five latency measures in specific time windows, which were collected from four different independent components. The independent components involved were a novelty component, a sensory related and two executive function related components. Using a 10-fold cross-validation approach, classification accuracy was 92%.
This study was a first attempt to classify ADHD adults by means of support vector machine which indicates that classification by means of non-linear methods is feasible in the context of clinical groups. Further, independent ERP components have been shown to provide features that can be used for characterizing clinical populations.
Saccadic eye movements align the two eyes precisely to foveate a target. Trial-by-trial variance of eye movement is always observed within an identical experimental condition. This has often been treated as experimental error without addressing its significance. The present study examined statistical linkages between the two eyes’ movements, namely interocular yoking, for the variance of eye position and velocity.
Horizontal saccadic movements were recorded from twelve right-eye-dominant subjects while they decided on saccade direction in Go-Only sessions and on both saccade execution and direction in Go/NoGo sessions. We used infrared corneal reflection to record simultaneously and independently the movement of each eye. Quantitative measures of yoking were provided by mutual information analysis of eye position or velocity, which is sensitive to both linear and non-linear relationships between the eyes’ movements. Our mutual information analysis relied on the variance of the eyes movements in each experimental condition. The range of movements for each eye varies for different conditions so yoking was further studied by comparing GO-Only vs. Go/NoGo sessions, leftward vs. rightward saccades.
Mutual information analysis showed that velocity yoking preceded positional yoking. Cognitive load increased trial variances of velocity with no increase in velocity yoking, suggesting that cognitive load may alter neural processes in areas to which oculomotor control is not tightly linked. The comparison between experimental conditions showed that interocular linkage in velocity variance of the right eye lagged that of the left eye during saccades.
We conclude quantitative measure of interocular yoking based on trial-to-trial variance within a condition, as well as variance between conditions, provides a powerful tool for studying the binocular movement mechanism.
Identifying eye movement related areas in the frontal lobe has a long history, with microstimulation in monkeys producing the most clear-cut results. For humans, however, there is still no consensus about the location and the extent of the frontal eye field (FEF). There is also no simple non-invasive method for unambiguously defining the FEF in individual subjects, a prerequisite for clinical applications. Here we explore the use of magnetoencephalography (MEG) for the non-invasive identification and characterization of FEF activity in an individual subject.
We mapped human brain activity before, during and after saccades by applying tomographic analysis to MEG data. Statistical parametric maps and circular statistics produced plausible FEF loci, but no unambiguous definition for individual subjects. Here we first computed the spectral decomposition and correlation with electrooculogram (EOG) of the tomographic brain activations. For each of these two measures statistical comparisons were made between different saccades.
In this paper, we first review the frontal cortex activations identified in earlier animal and human studies and place the putative human FEFs in a well-defined anatomical framework. This framework is then used as reference for describing the results of new Fourier analysis of the tomographic solutions comparing active saccade tasks and their controls. The most consistent change in the dorsal frontal cortex was at the putative left FEF, for both saccades to the left and right. The asymmetric result is consistent with the 1-way callosal traffic theory. We also showed that the new correlation analysis had its most consistent change in the contralateral putative FEF. This result was obtained for EOG latencies before saccade onset with delays of a few hundreds of milliseconds (FEF activity leading the EOG) and only for visual cues signaling the execution of a saccade in a previously defined saccade direction.
The FEF definition derived from microstimulation describes only one of the areas in the dorsal lateral frontal lobe that act together to plan, prepare and execute a saccade. The definition and characterization of these areas in an individual subject can be obtained from non-invasive MEG measurements.
A COST Action is a consortium of -mainly- European scientists (but open to international cooperation) working on a common research area, with the same subject; COST provides funding to the Actions for networking and dissemination activities, thus the participating scientists must have secured research funding from other national or European sources. COST funding is in the scale of approximately 100 kEuros per year and in this vein, it is often criticized both in that it does not fund research and the core science and in that its funding is ‘limited’. However, COST with its instruments is an integral pillar of the European Research Area, and it is through its mission that a variety of aspects of the research environment, fundamental to the success of the research, are catered for; these include scientific networking, collaboration/exchange/training and dissemination activities. Through fast procedures, proposals are evaluated and approved for funding in less than one year from submission date and Actions become operational immediately, managed on flexible management. In this way, COST contributes to reducing the fragmentation in European research investments, while opening the European Research Area to cooperation worldwide. COST Actions have an excellent record of building the critical mass for follow up activities in the EU FP or other similarly competitive programmes.
Pattern of brain asymmetries varies with handedness, gender, age, and with variety of genetic and social factors. Large-scale neuroimaging analyses can optimize the detection of asymmetric features and confirm the factors that might modulate pattern of brain asymmetries. We attempted to evaluate eventual differences between genders in hemodynamic responses to a simple language task.
12 healthy right-handed volunteers (age 24-46), 6 men and 6 women underwent fMRI scanning while performing the simple cognitive - language processing task – silent number counting in Serbian.
Group analysis of hemodynamic responses shows activation in expected brain language areas of inferior frontal gyrus (IFG) and superior temporal gyrus (STG) in both hemispheres. In the male group, aside from dedicated language areas in IFG and STG, activation was noted in right frontal region and interhemispheric supplementary motor area. On the other hand, in the female group, besides activation in dedicated language areas, activation was noted, in right hippocampus, limbic brain and cerebellum bilaterally.
Our results on differences in silent counting by means of fMRI suggest that those differences may be based on different brain pattern activation in men and women. The relation between performance, strategies and regional brain activation should be the topic of further studies when considering not only gender differences in language processing but also differences that may be attributed to the variations in the task details, stimuli, and the stimulus presentation methods.
In the present research we were interested to study the cerebral activity of a group of healthy subjects during the observation a documentary intermingled by a series of TV advertisements. In particular, we desired to examine whether Public Service Announcements (PSAs) are able to elicit a different pattern of activity, when compared with a different class of commercials, and correlate it with the memorization of the showed stimuli, as resulted from a following subject’s verbal interview.
We recorded the EEG signals from a group of 15 healthy subjects and applied the High Resolution EEG techniques in order to estimate and map their Power Spectral Density (PSD) on a realistic cortical model. The single subjects’ activities have been z-score transformed and then grouped to define four different datasets, related to subjects who remembered and forgotten the PSAs and to subjects who remembered and forgotten cars commercials (CAR) respectively, which we contrasted to investigate cortical areas involved in this encoding process.
The results we here present show that the cortical activity elicited during the observation of the TV commercials that were remembered (RMB) is higher and localized in the left frontal brain areas when compared to the activity elicited during the vision of the TV commercials that were forgotten (FRG) in theta and gamma bands for both categories of advertisements (PSAs and CAR). Moreover, the cortical maps associated with the PSAs also show an increase of activity in the alpha and beta band.
In conclusion, the TV advertisements that will be remembered by the experimental population have increased their cerebral activity, mainly in the left hemisphere. These results seem to be congruent with and well inserted in the already existing literature, on this topic, related to the HERA model. The different pattern of activity in different frequency bands elicited by the observation of PSAs may be justified by the existence of additional cortical networks processing these kind of audiovisual stimuli. Further research with an extended set of subjects will be necessary to further validate the observations reported in this paper.
The electroencephalography (EEG) is an attractive and a simple technique to measure the brain activity. It is attractive due its excellent temporal resolution and simple due to its non-invasiveness and sensor design. However, the spatial resolution of EEG is reduced due to the low conducting skull. In this paper, we compute the potential distribution over the closed surface covering the brain (cortex) from the EEG scalp potential. We compare two methods – L-curve and generalised cross validation (GCV) used to obtain the regularisation parameter and also investigate the feasibility in applying such techniques to N170 component of the visually evoked potential (VEP) data.
Using the image data set of the visible human man (VHM), a finite difference method (FDM) model of the head was constructed. The EEG dataset (256-channel) used was the N170 component of the VEP. A forward transfer matrix relating the cortical potential to the scalp potential was obtained. Using Tikhonov regularisation, the potential distribution over the cortex was obtained.
The cortical potential distribution for three subjects was solved using both L-curve and GCV method. A total of 18 cortical potential distributions were obtained (3 subjects with three stimuli each – fearful face, neutral face, control objects).
The GCV method is a more robust method compared to L-curve to find the optimal regularisation parameter. Cortical potential imaging is a reliable method to obtain the potential distribution over cortex for VEP data.
Information transmission and processing in the nervous system has stochastic nature. Multiple factors contribute to neuronal trial-to-trial variability. Noise and variations are introduced by the processes at the molecular and cellular level (thermal noise, channel current noise, membrane potential variations, biochemical and diffusion noise at synapses etc). The stochastic processes are affected by different physical (temperature, electromagnetic field) and chemical (drugs) factors. The aim of this study was experimental investigation of hypotheses that increase in the noise level in the brain affects processing of visual information. Change in the noise level was introduced by an external factor producing excess noise in the brain.
An exposure to 450 MHz low-frequency modulated microwave radiation was applied to generate excess noise. Such exposure has been shown to increase diffusion, alter membrane resting potential, gating variables and intracellular Calcium efflux. Nine healthy volunteers passed the experimental protocol at the lower (without microwave) and the higher (with microwave) noise level. Two photos (visual stimuli) of unfamiliar, young male faces were presented to the subjects, one picture after another. The task was to identify later the photos from a group of six photos and to decide in which order they were presented. Each subject had a total of eight sessions at the lower and eight at the higher noise level. Each session consisted of 50 trials; altogether a subject made 800 trials, 400 at the lower and 400 at the higher noise level. Student t-test was applied for statistical evaluation of the results.
Correct recognition of both stimuli in the right order was better at the lower noise level. All the subjects under investigation showed higher numbers of right answers in trials at the lower noise level. Average number of correct answers from n=400 trials with microwave exposure was 50.3, without exposure 54.4, difference 7.5%, p<0.002. No difference between results at the lower and the higher noise level was revealed in the case of only partly correct or incorrect answers.
Our experimental results showed that introduced excess noise reduced significantly ability of the nervous system in correct processing of visual information.
The study of brain functioning is a major challenge in neuroscience fields as human brain has a dynamic and ever changing information processing. Case is worsened with conditions where brain undergoes major changes in so-called different conscious states. Even though the exact definition of consciousness is a hard one, there are certain conditions where the descriptions have reached a consensus. The sleep and the anesthesia are different conditions which are separable from each other and also from wakefulness. The aim of our group has been to tackle the issue of brain functioning with setting up similar research conditions for these three conscious states.
In order to achieve this goal we have designed an auditory stimulation battery with changing conditions to be recorded during a 40 channel EEG polygraph (Nuamps) session. The stimuli (modified mismatch, auditory evoked etc.) have been administered both in the operation room and the sleep lab via Embedded Interactive Stimulus Unit which was developed in our lab. The overall study has provided some results for three domains of consciousness. In order to be able to monitor the changes we have incorporated Bispectral Index Monitoring to both sleep and anesthesia conditions.
The first stage results have provided a basic understanding in these altered states such that auditory stimuli have been successfully processed in both light and deep sleep stages. The anesthesia provides a sudden change in brain responsiveness; therefore a dosage dependent anesthetic administration has proved to be useful. The auditory processing was exemplified targeting N1 wave, with a thorough analysis from spectrogram to sLORETA. The frequency components were observed to be shifting throughout the stages. The propofol administration and the deeper sleep stages both resulted in the decreasing of N1 component. The sLORETA revealed similar activity at BA7 in sleep (BIS 70) and target propofol concentration of 1.2 µg/mL.
The current study utilized similar stimulation and recording system and incorporated BIS dependent values to validate a common approach to sleep and anesthesia. Accordingly the brain has a complex behavior pattern, dynamically changing its responsiveness in accordance with stimulations and states.
It has been discussed that neural phase-synchrony across distant cortical areas (or global phase-synchrony) was correlated with various aspects of consciousness. The generating process of the synchrony, however, remains largely unknown. As a first step, we investigate transient process of global phase-synchrony, focusing on phase-synchronized clusters. We hypothesize that the phase-synchronized clusters are dynamically organized before global synchrony and clustering patterns depend on perceptual conditions.
In an EEG study, Kitajo reported that phase-synchrony across distant cortical areas was selectively enhanced by top-down attention around 4 Hz in Necker cube perception. Here, we further analyzed the phase-synchronized clusters using hierarchical clustering which sequentially binds up the nearest electrodes based on similarity of phase locking between the cortical signals. First, we classified dominant components of the phase-synchronized clusters over time. We then investigated how the phase-synchronized clusters change with time, focusing on their size and spatial structure.
Phase-locked clusters organized a stable spatial pattern common to the perceptual conditions. In addition, the phase-locked clusters were modulated transiently depending on the perceptual conditions and the time from the perceptual switch. When top-down attention succeeded in switching perception as subjects intended, independent clusters at frontal and occipital areas grew to connect with each other around the time of the perceptual switch. However, the clusters in the occipital and left parietal areas remained divided when top-down attention failed in switching perception. When no primary biases exist, the cluster in the occipital area grew to its maximum at the time of the perceptual switch within the occipital area.
Our study confirmed the existence of stable phase-synchronized clusters. Furthermore, these clusters were transiently connected with each other. The connecting pattern depended on subjects’ internal states. These results suggest that subjects’ attentional states are associated with distinct spatio-temporal patterns of the phase-locked clusters.