Correlation between the parameters of contingent negative variation and characteristics of variational pulsometry in Parkinsonian patients

In patients suffering from Parkinson's disease (PD), we analyzed correlations between the parameters of contingent negative variation (CNV) and data of variational pulsometry (according to the measurements of R-R ECG intervals). Studies were carried out on 35 patients (group PD), 49 to 74 years old, with the stage of disease of 1.5 to 3.0 according to the Hoehn-Yahr international classification. In the course of CNV recording (i.e., in the state of a certain functional loading), we observed significant negative correlations between the integral magnitude (area) of this potential and indices of variational pulsometry (RMSSD, SDNN, C. var, and HF) that characterize the intensity of parasympathetic (respiratory) influences on the cardiovascular system. In the control group, such correlations were absent. We found significant correlations between the autonomic balance, CNV magnitude, and stage of PD reflecting the level of generalization of the pathological process. In the subgroup of patients with the PD stage 1.5 to 2.0, significant changes in the mean values of indices of parasympathetic influences during recording of the CNV were not observed, while in another subgroup (the PD stage 2.5 to 3.0), these values increased significantly (P < 0.05 and P < 0.01). If the estimates of the PD stage were low, the CNV area demonstrated greater values (P < 0.01). The disturbance of coordination of muscle-to-muscle interactions in the PD group is, probably, an important factor responsible for parasympathetic dysregulation and suppression of the CNV generation. We found positive correlation between the intensity of parasympathetic influences in the course of CNV recording and the level of postural disorders (r = 0.37, P < 0.05). On the contrary, the CNV magnitude demonstrated a negative correlation with the intensity of these disorders (r = −0.36, P < 0.05), as well as with the level of postural instability (r = −0.55, P < 0.001). We hypothesize that alterations of the autonomic balance and the activity of those cerebral structures, which are responsible for the motor readiness, result, to a significant extent, from weakening of the activity of the noradrenergic system due to degenerative processes developing in cells of the locus coeruleus. The impairment of the latter structure, together with degeneration of neurons of the substantia nigra and a decrease in the level of nigro-striatal dopamine, underlies the pathomorphological pattern of PD. Neirofiziologiya/Neurophysiology, Vol. 40, No. 3, pp. 242–253, May–June, 2008.     

Correlations between the psychological peculiarities of an individual and the efficacy of a single neurofeedback session (by the EEG characteristics)

Modifications of different EEG rhythms induced by a single neurofeedback session (by the EEG characteristics) directed toward an increase in the ratio of the spectral powers (SPs) of the α vs θ oscillations were compared with the psychological characteristics of the tested subjects (the group included 30 persons). A generally accepted neurofeedback technique was used; the intensity of acoustic white noise served as the feedback signal. EEG potentials were recorded from the C3 and C4 leads. Psychological testing was carried out using Eysenck’s (EPQ), Rusalov’s (OST), and (16 PF) questionnaires. The directions of changes in the SPs of EEG frequency components were found to significantly correlate with some individuality-related peculiarities of the tested subjects. The SP of the δ rhythm correlated with the EPQ scale “neuroticism,” OST scale “social plasticity,” and 16 PF factors H (“parmia”), I (“premsia”), and Q₃ (“self-control of behavior”). The SP of the θ component demonstrated correlations with the OST scales “ergisity,” “plasticity,” and “social temp” and with 16 PF factors M (“autia”), Q₄ (“frustration”), and Q1 (“exvia”). The SP of the α rhythm correlated with 16 PF factors Q₃ (“self-control of behavior”), G (“strength of superEgo”), O (“hypothymia”), L (“protension”), and N (“shrewdness”). The SP of the β rhythm correlated with the OST scale “emotionality,” while that of the γ rhythm showed correlations with the 16 PF indices L (“protension”) and M (“autia”). Changes in the ratio of the α vs θ SPs correlated with the EPQ scale “neuroticism.” Thus, our data confirm the statement that a high individual variability of the results of a single (first in the series) neurofeedback session is to a great extent related to peculiarities of the individual psychological pattern of the subject. Neirofiziologiya/Neurophysiology, Vol. 38, No. 3, pp. 239–247, May–June, 2006.     

Neural processes underlying statistical learning for speech segmentation in dogs

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The Effects of Alpha (10-Hz) and Beta (22-Hz) “Entrainment” Stimulation on the Alpha and Beta EEG Bands: Individual Differences Are Critical to Prediction of Effects

Two different groups of normal college students were formed: One (the alpha group) received 10-Hz audiovisual (AV) stimulation for 8 minutes, and the other (beta) group received 22-Hz AV stimulation for 8 minutes. EEG power in the alpha (8-13 Hz) and beta (13-30 Hz) bands was FFT-extracted before, during, and for 24 minutes after stimulation. It was found that baseline (prestimulation) alpha and beta power predict the effects of stimulation, leading to individual differences in responsivity. High-baseline alpha participants showed either no entrainment or relatively prolonged entrainment with alpha stimulation. Low-baseline participants showed transient entrainment. Baseline alpha also predicted the direction of change in alpha with beta stimulation. Baseline beta and alpha predicted beta band response to beta stimulation, which was transient enhancement in some participants, inhibition in others. Some participants showed relatively prolonged beta enhancement with beta stimulation.     

Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.     

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.     

Evaluation of TV commercials using neurophysiological responses

Background

In recent years, neuroscientific knowledge has been applied to marketing as a novel and efficient means to comprehend the cognitive and behavioral aspects of consumers. A number of studies have attempted to evaluate media contents, especially TV commercials using various neuroimaging techniques such as electroencephalography (EEG). Yet neurophysiological examination of detailed cognitive and affective responses in viewers is still required to provide practical information to marketers. Here, this study develops a method to analyze temporal patterns of EEG data and extract affective and cognitive indices such as happiness, surprise, and attention for TV commercial evaluation.

Methods

Twenty participants participated in the study. We developed the neurophysiological indices for TV commercial evaluation using classification model. Specifically, these model-based indices were customized using individual EEG features. We used a video game for developing the index of attention and four video clips for developing indices of happiness and surprise. Statistical processes including one-way analyses of variance (ANOVA) and the cross validation scheme were used to select EEG features for each index. The EEG features were composed of the combinations of spectral power at selected channels from the cross validation for each individual. The Fisher’s linear discriminant classifier (FLDA) was used to estimate each neurophysiological index during viewing four different TV commercials. Post hoc behavioral responses of preference, short-term memory, and recall were measured.

Results

Behavioral results showed significant differences for all preference, short-term memory rates, and recall rates between commercials, leading to a ‘high-ranked’ commercial group and a ‘low-ranked’ group (P < 0.05). Neural estimation of happiness results revealed a significant difference between the high-ranked and the low-ranked commercials in happiness index (P < 0.01). The order of rankings based on happiness and attention matched well with the order of behavioral response rankings. In the elapsed-time analysis of the highest-ranked commercial, we could point to visual and auditory semantic structures of the commercial that induced increases in the happiness index.

Conclusions

Our results demonstrated that the neurophysiological indices developed in this study may provide a useful tool for evaluating TV commercials.