Nutritive sucking induces age-specific EEG-changes in 0-24 week-old infants.

Little is known how the brain of the newborn infant responds to the postnatal nutrition and care. No systematic studies exist in which the effects of nutritional and non-nutritional sucking on the brain activity of the infant were compared. We recorded the EEG activity of 40 infants at the ages of 0,6,12 and 24 weeks in four successive behavioral stages: while the infants were hungry and waiting for sucking, during non-nutritional and nutritional sucking, and during satiation after completed feeding. Quantitative EEG analysis was performed using occipital, parietal and central EEG channels. In the newborn infants, a significant reduction in the EEG power was found after nutritional sucking in the all EEG frequency bands studied (1-10Hz), which was paralleled by a significant behavioral alertness decline. This response decayed during the subsequent neonatal period and was completely absent at the age of 12 weeks. In 24-week-old infants, nutritional sucking was accompanied with an increase in rhythmic theta activity during which no significant alertness change took place. Non-nutritional sucking was connected with minor and non-significant effects on the EEG. We conclude that in newborn infants nutritional sucking has a direct effect on the EEG, which has a soothing character and is connected with an alertness decline. In 24-week-old infants the response to nutritional sucking is of a different type and consists of an organized, rhythmical theta activity in the EEG not directly linked with alertness change. Our findings suggest a developmental relationship between nursing and infant brain function with plausible affective and cognitive implications.

Nonlinear parameters of surface EMG in schizophrenia patients depend on kind of antipsychotic therapy.

UNLABELLED: We compared a set of surface EMG (sEMG) parameters in several groups of schizophrenia (SZ, n = 74) patients and healthy controls (n = 11) and coupled them with the clinical data. sEMG records were quantified with spectral, mutual information (MI) based and recurrence quantification analysis (RQA) parameters, and with approximate and sample entropies (ApEn and SampEn). Psychotic deterioration was estimated with Positive and Negative Syndrome Scale (PANSS) and with the positive subscale of PANSS. Neuroleptic-induced parkinsonism (NIP) motor symptoms were estimated with Simpson-Angus Scale (SAS). Dyskinesia was measured with Abnormal Involuntary Movement Scale (AIMS). We found that there was no difference in values of sEMG parameters between healthy controls and drug-naïve SZ patients. The most specific group was formed of SZ patients who were administered both typical and atypical antipsychotics (AP). Their sEMG parameters were significantly different from those of SZ patients taking either typical or atypical AP or taking no AP. This may represent a kind of synergistic effect of these two classes of AP. For the clinical data we found that PANSS, SAS, and AIMS were not correlated to any of the sEMG parameters. CONCLUSION: with nonlinear parameters of sEMG it is possible to reveal NIP in SZ patients, and it may help to discriminate between different clinical groups of SZ patients. Combined typical and atypical AP therapy has stronger effect on sEMG than a therapy with AP of only one class.

Directional connectivity between frontal and posterior brain regions is altered with increasing concentrations of propofol.

Recent studies using electroencephalography (EEG) suggest that alteration of coherent activity between the anterior and posterior brain regions might be used as a neurophysiologic correlate of anesthetic-induced unconsciousness. One way to assess causal relationships between brain regions is given by renormalized partial directed coherence (rPDC). Importantly, directional connectivity is evaluated in the frequency domain by taking into account the whole multichannel EEG, as opposed to time domain or two channel approaches. rPDC was applied here in order to investigate propofol induced changes in causal connectivity between four states of consciousness: awake (AWA), deep sedation (SED), loss (LOC) and return of consciousness (ROC) by gathering full 10/20 system human EEG data in ten healthy male subjects. The target-controlled drug infusion was started at low rate with subsequent gradual stepwise increases at 10 min intervals in order to carefully approach LOC (defined as loss of motor responsiveness to a verbal stimulus). The direction of the causal EEG-network connections clearly changed from AWA to SED and LOC. Propofol induced a decrease (p = 0.002-0.004) in occipital-to-frontal rPDC of 8-16 Hz EEG activity and an increase (p = 0.001-0.040) in frontal-to-occipital rPDC of 10-20 Hz activity on both sides of the brain during SED and LOC. In addition, frontal-to-parietal rPDC within 1-12 Hz increased in the left hemisphere at LOC compared to AWA (p = 0.003). However, no significant changes were detected between the SED and the LOC states. The observed decrease in back-to-front EEG connectivity appears compatible with impaired information flow from the posterior sensory and association cortices to the executive prefrontal areas, possibly related to decreased ability to perceive the surrounding world during sedation. The observed increase in the opposite (front-to-back) connectivity suggests a propofol concentration dependent association and is not directly related to the level of consciousness per se.

Heart rate variability dynamics during low-dose propofol and dexmedetomidine anesthesia.

Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia.

Tracking single-trial evoked potential changes with Kalman filtering and smoothing.

A mathematical way to describe trial-to-trial variations in evoked potentials (EPs) is given by state-space modeling. Linear estimators optimal in the mean square sense can then be obtained through Kalman filter and smoother algorithms. Of importance are the parametrization of the problem and the selection of an observation model for estimation. In this paper, we introduce a general way for designing a model for dynamical estimation of EPs. The observation model is constructed based on a finite impulse response (FIR) filter and can be used for different kind of EPs. We also demonstrate that for batch processing the use of the smoother algorithm is preferable. The method is demonstrated with measurements obtained from an experiment with visual stimulation.

Estimation of single-trial fMRI BOLD responses using combined EEG and fMRI measurements.

In this paper, we present a method for modeling human brain response using combined fMRI and EEG measurements. A subspace is formed using the eigenvectors of data correlation matrix of augmented measurements. This subspace is then used for regularization of the fitting of parametric model to fMRI BOLD signal. The approach is utilized for single-trial estimation of blood oxygenation level dependent (BOLD) responses in fMRI time series.

Effects of nutritive and non-nutritive sucking on infant heart rate variability during the first 6 months of life.

The effects of eating on heart rate variability (HRV) differ between adults and newborns. This may reflect the impact of suckling on the overall psychophysiological and autonomic nervous system maturation. The purpose of the present study was to explore whether the reactions of HRV during feeding change towards the adult pattern during the first 6 months of life. In addition, the effects of non-nutritive and nutritive sucking on heart rate (HR) and HRV were compared. The participants were 23 infants on whom recordings were performed as newborns and at 6, 12 and 24 weeks old. Nutritive sucking caused an increase in HR and a decline in HRV. The results were consistent with previous reports of a decrease in high frequency components of HRV during feeding in newborns, reflecting a decrease in parasympathetic activity. This response was apparent in all four ages studied, and remained similar throughout the 6-month period. However, age as an independent factor seemed to influence both HR and HRV. Pacifier sucking had no significant effects on HRV at any age. The results demonstrate the physical strain that sucking imposes on the baby, with a specific autonomic nervous system response involved. We consider this response an essential part of the overall psychophysiological maturation of infants.

A subspace method for dynamical estimation of evoked potentials.

It is a challenge in evoked potential (EP) analysis to incorporate prior physiological knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP characteristics estimation. Prior information about phase-locked properties of the EPs is assesed by means of estimated signal subspace and eigenvalue decomposition. Then for those situations that dynamic fluctuations from stimulus-to-stimulus could be expected, prior information can be exploited by means of state-space modeling and recursive Bayesian mean square estimation methods (Kalman filtering and smoothing). We demonstrate that a few dominant eigenvectors of the data correlation matrix are able to model trend-like changes of some component of the EPs, and that Kalman smoother algorithm is to be preferred in terms of better tracking capabilities and mean square error reduction. We also demonstrate the effect of strong artifacts, particularly eye blinks, on the quality of the signal subspace and EP estimates by means of independent component analysis applied as a prepossessing step on the multichannel measurements.

Time-varying analysis of heart rate variability signals with a Kalman smoother algorithm.

A time-varying parametric spectrum estimation method for analysing non-stationary heart rate variability signals is presented. As a case study, the dynamics of heart rate variability during an orthostatic test is examined. In this method, the non-stationary signal is first modelled with a time-varying autoregressive model and the model parameters are estimated recursively with a Kalman smoother algorithm. The benefit of using the Kalman smoother is that the lag error present in a Kalman filter, as well as in all other adaptive filters, can be avoided. The spectrum estimates for each time instant are then obtained from the estimated model parameters. Statistics of the obtained spectrum estimates are derived using the error propagation principle. The obtained spectrum estimates can further be decomposed into separate components and, thus, the time variation of low- and high-frequency components of heart rate variability can be examined separately. By using the presented method, high resolution time-varying spectrum estimates with no lag error can be produced. Other benefits of the method are the straightforward procedure for evaluating the statistics of the spectrum estimates and the option of spectral decomposition.

Single-trial dynamical estimation of event-related potentials: a Kalman filter-based approach.

A method for single-trial dynamical estimation of event-related potentials (ERPs) is presented. The method is based on recursive Bayesian mean square estimation and the estimators are obtained with a Kalman filtering procedure. We especially focus on the case that previous trials contain prior information of relevance to the trial being analyzed. The potentials are estimated sequentially using the previous estimates as prior information. The performance of the method is evaluated with simulations and with real P300 responses measured using auditory stimuli. Our approach is shown to have excellent capability of estimating dynamic changes form stimulus to stimulus present in the parameters of the ERPs, even in poor signal-to-noise ratio (SNR) conditions.