The structure of the background EEG of the rabbit in the high-frequency portion of the spectrum]

With the purpose of studying the character and structure of high frequency bioelectric activity of rabbits cerebral cortex in the state of calm alertness, the EEG ensembles of different areas of the cortex (sensorimotor, visual, acoustic) and dorsal hippocampus were studied with FFT method. A supposition was made about the presence of systemic organization of the background EEG in rabbits cerebral cortex, reflected, in particular, in the presence of determined components both of chaotic and rhythmic character having different degrees of manifestation. Heterogeneity was revealed in distribution of energies of spectral EEG components in the studied frequency ranges from 14.7 to 100 Hz with predominance of total specific energy value in the band of 14.7-60 Hz. In coherence functions of all the studied pairs of EEG leads rhythmic component, stable in time, was absent. Functions of the mean EEG coherence in the band of 61-100 Hz had significantly greater values in comparison with the values in the band of 14.7-40 Hz.     

Comparison of reactive EEG changes and fMRI characteristics of brain health based on multivariate statistics

To gain a deeper insight into the relationship between the electrogenesis and oxygenation of the brain, fMRI and EEG reactions to identical functional loads (opening of the eyes and right- and left-hand fingering) were compared in 11 young right-handed healthy subjects with statistical techniques. Changes in power, frequency and coherent EEG parameters obtained by 18-channel monopolar recording were compared with values of + BOLD-fMRI response, calculated for 18 corresponding cortical areas on the basis of application of the "virtual cap" by the original algorithm. In reactive changes of both hemodynamic and bioelectrical parameters, sets of independent factors were identified, which were regarded on the basis of their topography as specific (localized in the cortical representation ofa relevant analyzer) and nonspecific (diffuse and similar under different functional loads). Specific component dominated in the fMRI response, whereas non-specific component was characteristic of the EEG reaction. The similar topography of reactive fMRI and EEG factors under normal conditions, confirmed by the correlation analysis, reflects the multilevel character of the systemic organization of the brain activity, visualized, in particular, in the sagittal projections of the individual fMRI images. Each of the reactive EEG factors included all of the EEG quantitative characteristics. EEG coherence, which dominated among other parameters (with a local increase in the cortical representation of a relevant analyzer and a diffuse decrease in the areas of the influence of the regulatory structures) displayed the highest correlation with hemodynamic responses of the brain.     

Spontaneous Slow Fluctuation of EEG Alpha Rhythm Reflects Activity in Deep-Brain Structures: A Simultaneous EEG-fMRI Study

The emergence of the occipital alpha rhythm on brain electroencephalogram (EEG) is associated with brain activity in the cerebral neocortex and deep brain structures. To further understand the mechanisms of alpha rhythm power fluctuation, we performed simultaneous EEGs and functional magnetic resonance imaging recordings in human subjects during a resting state and explored the dynamic relationship between alpha power fluctuation and blood oxygenation level-dependent (BOLD) signals of the brain. Based on the frequency characteristics of the alpha power time series (APTS) during 20-minute EEG recordings, we divided the APTS into two components: fast fluctuation (0.04–0.167 Hz) and slow fluctuation (0–0.04 Hz). Analysis of the correlation between the MRI signal and each component revealed that the slow fluctuation component of alpha power was positively correlated with BOLD signal changes in the brain stem and the medial part of the thalamus and anterior cingulate cortex, while the fast fluctuation component was correlated with the lateral part of the thalamus and the anterior cingulate cortex, but not the brain stem. In summary, these data suggest that different subcortical structures contribute to slow and fast modulations of alpha spectra on brain EEG.     

Topographic analysis of dimension estimates of EEG and filtered rhythms in epileptic patients with complex partial seizures

Nonlinear dynamic properties were analyzed on the EEG and filtered rhythms recorded from healthy subjects and epileptic patients with complex partial seizures. Estimates of correlation dimensions of control EEG, interictal EEG and ictal EEG were calculated. The values were demonstrated on topograms. The delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–40 Hz) components were obtained and considered as signals from the cortex. Corresponding surrogate data was produced. Firstly, the influence of sampling parameters on the calculation was tested. The dimension estimates of the signals from the frontal, temporal, parietal and occipital regions were computed and compared with the results of surrogate data. In the control subjects, the estimates between the EEG and surrogate data did not differ (P > 0.05). The interictal EEG from the frontal region and occipital region, as well as its theta component from the frontal region, and temporal region, showed obviously low dimensions (P < 0.01). The ictal EEG exhibited significantly low-dimension estimates across the scalp. All filtered rhythms from the temporal region yielded lower results than those of the surrogate data (P < 0.01). The dimension estimates of the EEG and filtered components markedly changed when the neurological state varied. For each neurological state, the dimension estimates were not uniform among the EEG and frequency components. The signal with a different frequency range and in a different neurological state showed a different dimension estimate. Furthermore, the theta and alpha components demonstrated the same estimates not only within each neurological state, but also among the different states. These results indicate that the theta and alpha components may be caused by similar dynamic processes. We conclude that the brain function underlying the ictal EEG has a simple mechanism. Several heterogeneous dynamic systems play important roles in the generation of EEG. Received: 10 December 1999 / Accepted in revised form: 8 May 2000     

Dependence of the organization of human brain electrical activity on hemispheric dominance

In healthy subjects, in a state of relative rest, with different individual profiles of asymmetry (20 right-handed and 10 left-handed subjects), a greater conjunction of electrical brain processes (estimated by mean EEG coherence levels) has been found in the dominant hemisphere as compared to the subdominant one, more distinctly expressed in the right-handed subjects. The maximum degree of interhemispheric asymmetry of the EEG coherence is observed in the posterior associative cortical areas. Specific interhemispheric theta-range differences are revealed as compared with other EEG frequency bands. Greater values are obtained of the correlation of the EEG symmetrical hemispheres points in the whole frequency band and in alpha- and beta-ranges in the right-handed subjects than in the left-handed ones.     

Effect of Individual Features of the CNS on Efficiency of Relaxation Biofeedback Training in 9- to 10-Year-Old Children

The possibilities of biofeedback training for improvement of the self-control of the functional state (relaxation) were studied in 9- to 10–year-old children. At the first stage, under conditions of electrophysiological experiment, relaxation shifts were assessed in the cycle quiet wakefulness–relaxation–recovery of the initial state by autonomic (skin resistance) and EEG (spectra and coherence) indices. The children were then trained to control their functional state with a computer game including a feedback loop by skin temperature. After the training cycle, children were repeatedly examined in electrophysiological experiment with the instruction to control their state. Comparative analysis of self-induced relaxation changes before and after a successful training course revealed greater shifts of skin resistance and an increase in the number of distant functional connections (especially, in the intermediate and high-frequency EEG subbands), with a significantly increased coherence level during relaxation. A correlation was found between the efficiency of self-regulation training and some individual psychophysiological characteristics (simple motor reaction time, autonomic coefficient, resting EEG). Low efficiency of self-control training was observed in younger schoolchildren with a sharply deviant (from the mean group values) reaction time and autonomic coefficient, as well as with EEG manifestations of functional immaturity of the upper brain regulatory structures. The dependence of the EEG changes on the self-regulation strategy is discussed on the basis of obtained evidence and data in the literature.     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.     

Nonlinear analysis of EEG signals: Surrogate data analysis

ObjectivesThe electroencephalogram (EEG) signal contains information about the state and condition of the brain. The aim of the study is to conduct a nonlinear analysis of the EEG signals and to compare the differences in the nonlinear characteristics of the EEG during normal state and the epileptic state.DataThe EEG data used for this study – which consisted of epileptic EEG and normal EEG – were obtained from the EEG database available with the Bonn University, Germany.ResultsThe attractors seen in normal and epileptic human brain dynamics were studied and compared. Surrogate data analyses were conducted on two nonlinear measures, namely the largest Lyapunov exponent and the correlation dimension, to test the hypothesis whether EEG signals were in accordance with linear stochastic models.DiscussionsThe existence of deterministic chaos in brain activity is confirmed by the existence of a chaotic attractor; also, saturation of the correlation dimension towards a definite value is the manifestation of a deterministic dynamics. Also a reduction is observed between the dimensionalities of the brain attractors from normal state to the epileptic state. The evaluation of the largest Lyapunov exponent also confirms the lowering of complexity during an episode of seizure.ConclusionIn case of Lyapunov exponent of EEG data, the change due to surrogating is small suggesting that it is not representing the system complexity properly but there is a marked change in the case of correlation dimension value due to surrogating.     

The reorganization of the electrical activity of the human brain during the depression of consciousness (comatose states)

To study the problem of consciousness an original structural-functional approach has been applied with the use of possibilities of spectral-coherent EEG analysis in evaluation of human brain functional state together with the specificity of the cerebral coma having local focal belonging. It is revealed that the most informative signs for characteristics of consciousness state are peculiarities of reconstruction of intercentral relations of the electrical brain processes: decrease of mean levels of the EEG coherence at progressive development of coma; staged approach to relative norm at regressive course of comatose state and recovery of consciousness; their stability on the low level at prolonged coma; increase in low or high (above optimum) frequencies band at neighbouring with coma states of consciousness. It may be considered that one of the necessary conditions of normal state of consciousness is preservation of the optimum level of correlation of electrical brain activity alongside with frequency-regional specificity of the EEG coherence spectrum. Any deviation from the optimum is unfavourable condition for normal course of cerebral reactions because of the disturbance of intercentral connections mosaic necessary for their realization.     

Asymmetry of the bioelectrical activity of the human brain at different levels of consciousness

Asymmetry of different human EEG indices was studied at different levels of consciousness. Subjects' self-reported changes in the content of consciousness: the intensity and quality of involuntary mental processes served as indicator of the level of consciousness. It was shown that a certain profile of EEG asymmetry corresponded to each the observed level of consciousness. In active state of consciousness, the connections in the high-frequency bands: beta-2 and gamma, were more pronounced in the left hemisphere of the brain. At the same time, transition of the focus of coherent connections to the right hemisphere was characteristic of the state of inhibition of "internal speech". The interhemisphere dynamics of autospectra amplitude and foci of coherent connections supports the notion that the character of interhemisphere asymmetry of the brain bioelectrical activity depends on its functional state.     

Multiple parameter comparative EEG analysis in alcoholism and narcotic dependence

Multiparametric comparative analysis of spatial organization of EEG was carried out in 137 alcoholics and 131 heroin addicts. Common and different deviations from normal EEG (105 control subjects) were found. Global alterations of EEG spatial organization were observed in drug addicts (as compared to alcoholics). Such changes characterized increasing synchronizing effects of mesolimbic and brainstem structures on the brain cortex. The ethanol effects were more specific and asymmetric. Changes in EEG spectral-coherence characteristics were revealed in all frequency band, however, maximal changes took place in the high-frequency theta in drug addicts and in narrow-frequency alpha subranges in alcoholics. Different effects on the high-frequency EEG component (19.00-21.25 Hz) and information-energy index (coherence-to-spectral power ratio) suggest the difference influence of ethanol and heroin on emotional-motivational and cognitive processes as well as the level of consciousness. The obtained data on EEG discrimination of alcoholism and drug addiction (the inverse problem solution) on the basis of "specific" EEG patterns appear to have considerable promise in development of systems of occupational selection.     

Generalized dimension of the intersection between EEGs

 The generalized dimension defined by [Mandelbrot (1995) J Fourier Anal Appl special J.P. Kahane issue: 409–432] was applied to studying the interrelationship between various parts of human cerebral cortex in different functional conditions. Taking EEG signals from different brain areas as different sets, the generalized dimensions of their intersections were calculated to describe the interrelationship between them. The results showed that the generalized dimensions of intersections in different brain states decreased according to the following order: rest with eyes open, closed, light sleep, and deep sleep. The generalized dimensions of intersections related to the left or right temporal lobe were higher than the others when the subjects was doing mental arithmetic, and there was a decrease when the subjects listened to soft classical music. In addition, it was found that there was a noticeable difference in singular spectra between epileptic patients and normal subjects, irrespective of whether the epileptic patient was experiencing a seizure or not. Received: 3 July 2000 / Accepted in revised form: 30 October 2000     

Multivariate EEG spectral analysis evidences the functional link between motor and visual cortex during integrative sensorimotor tasks

The identification of the networks connecting brain areas and the understanding of their role in executing complex tasks is a crucial issue in cognitive neuroscience. In this study, specific visuomotor tasks were devised to reveal the functional network underlying the cooperation process between visual and motor regions. Electroencephalography (EEG) data were recorded from twelve healthy subjects during a combined visuomotor task, which integrated precise grip motor commands with sensory visual feedback (VM). This condition was compared with control tasks involving pure motor action (M), pure visual perception (V) and visuomotor performance without feedback (V + M). Multivariate parametric cross-spectral analysis was applied to ten EEG derivations in each subject to assess changes in the oscillatory activity of the involved cortical regions and quantify their coupling. Spectral decomposition was applied to precisely and objectively determine the power associated with each oscillatory component of the spectrum, while surrogate data analysis was performed to assess the statistical significance of estimated coherence values. A significant decrease of the alpha and/or beta power in EEG spectra with respect to rest values was assumed as indicative of specific cortical area activation during task execution. Indeed alpha band coherence increased in proximity of task-involved areas, while it was suppressed or remained unchanged in other regions, suggesting the activation of a specific network for each task. According to our coherence analysis, a direct link between visual and motor areas was activated during V + M and VM tasks. The effect of visual feedback was evident in the beta band, where the increase of coherence was observed only during the VM task. Multivariate analysis suggested the presence of a functional link between motor and visual cortex subserving sensorimotor integration. Furthermore, network activation was related to the sum of single task (M and V) local effects in the alpha band, and to the presence of visual feedback in the beta band.     

Functional interrelations of brain structures in the cat during the generation of rhythmic activity. III. Cluster analysis of rhythmic indices

The functional interrelationships of the brain structures of freely moving cats in generation of rhythmic EEG activity during the states of drowsiness and light sleep were evaluated using the claster analysis of mean values indexes of rhythms in different structures as well as correlation coefficients between them in time. It was shown that according these parameters visual cortical areas and lateral geniculate body appeared in different clusters. Lateral geniculate body suggested not to be the only pacemaker of EEG rhythms in visual cortex. The wide convergence of subcortical inputs to the visual cortex and possibility of autonomic generation of EEG rhythms at the cortical level are discussed as putative mechanisms of dissociation of EEG activities in visual cortex and thalamus.     

Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy

Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of “cortico-thalamo-cortical network” in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.     

Post-radiation effect on the interhemispheric asymmetry in EEG and thermography characteristics

Complex analysis of EEG and thermographic parameters carried out in 10 healthy subjects and 34 patients, Chernobyl clean-up participants revealed a correlation between EEG and brain temperature changes in the baseline state and during mental arithmetic. During cognitive activity the maximal increase in the average EEG coherence and temperature shifts in healthy subjects were observed in the left frontotemporal and right parietotemporal areas. In patients changes in both parameters under study were most pronounced, the interhemispheric relations were impaired. The visual analysis revealed "flat" and "hypersynchronous" EEG types in patients. The dominant pathologic activity in the betal range indicative of mediobasal and oral brainstem lesions was characteristic of the flat EEG. This type of activity was observed in 60% of patients. In these cases, a general decrease in EEG coherence and temperature was most pronounced in the left hemisphere. The hypersynchronou EEG type (40% patients) was characterized by paroxysmal activity in the theta and alpha ranges suggesting diencephalic brain lesions. In these cases, EEG coherence and temperature were more variable; changes in the right hemisphere were significant, be it increase or decrease. Our complex approach to investigation of brain activity in different aspects seems to be promising in estimation of the brain functional state both in healthy persons and patients in remote terms after exposure to radiation. The specific hemispheric temperature changes revealed in Chernobyl patients especially during cognitive activity can be the sequels of postradiation disorders of vascular neuro-circulation. The EEG findings suggest subcortical disorders at different levels (diencephalic or brainstem) and functional failure of the right or left hemispheres in remote terms after exposure to radiation.     

Power spectral density and coherence analysis of Alzheimer’s EEG

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer’s disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.     

Functional Cortical Network in Alpha Band Correlates with Social Bargaining

Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts.     

基于空间ICA和时间相关方法的人脑视觉皮层V5区的功能连通性研究

利用功能磁共振成像技术,将空间ICA和时间相关方法相结合来研究不同活动状态下人脑视觉皮层V5区的功能连通性。首先利用空间ICA处理组块视觉运动刺激的数据,定位V5区;然后分别计算静息和连续视觉运动刺激两种稳态下V5区与其它脑区低频振荡的时间相关,检测出该区的功能连通网络。实验结果表明,静息时V5区的功能连通网络更广泛,且与已知的解剖连通一致;当被试接受连续视觉运动刺激时,与V5区连通的脑区网络局限在视觉皮层,此时的网络特定于处理视觉运动这一任务。