Karin Schwab

Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems

Over the last decades several techniques have been developed to analyze interactions in multivariate dynamic systems. These analysis techniques have been applied to empirical data recorded in various branches of research, ranging from economics to biomedical sciences. Investigations of interactions between different brain structures are of strong interest in neuroscience. The information contained in electromagnetic signals may be used to quantify the information transfer between those structures. When investigating such interactions, one has to face an inverse problem. Usually the distinct features and different conceptual properties of the underlying processes generating the empirical data and therefore the appropriate analysis technique are not known in advance. The performance of these methods has mainly been assessed on the basis of those model systems they have been developed for. To draw reliable conclusions upon application to empirical time series, understanding the properties and performances of the time series analysis techniques is essential. To this aim, the performances of four representative multivariate linear signal processing techniques in the time and frequency domain have been investigated in this study. The partial cross-spectral analysis and three different quantities measuring Granger causality, i.e. a Granger causality index, partial directed coherence, and the directed transfer function are compared on the basis of different model systems. To capture distinct properties in the dynamics of brain neural networks, we have investigated multivariate linear, multivariate nonlinear as well as multivariate non-stationary model systems. In an application to neural data recorded by electrothalamography and electrocorticography from juvenile pigs under sedation, directed as well as time-varying interactions have been studied between thalamic and cortical brain structures. The time-dependent alterations in local activity and changes in the interactions have been analyzed by the Granger causality index and the partial directed coherence. Both methods have been shown to be most suitable for this application to brain neural networks based on our model systems investigated. The results of this investigation contribute to the long-term goal to understand the relationships in neural structures in an abnormal state of deep sedation.

Modeling brain resonance phenomena using a neural mass model

Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM) of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect.

Fetal body weight and the development of the control of the cardiovascular system in fetal sheep

Reduced birth weight predisposes to cardiovascular diseases in later life. We examined in fetal sheep at 0.76 (n= 18) and 0.87 (n= 17) gestation whether spontaneously occurring variations in fetal weight affect maturation of autonomic control of cardiovascular function. Fetal weights at both gestational ages were grouped statistically in low (LW) and normal weights (NW) (P < 0.01). LW fetuses were within the normal weight span showing minor growth dysproportionality at 0.76 gestation favouring heart and brain, with a primary growth of carcass between 0.76 and 0.87 gestation (P < 0.05). While twins largely contributed to LW fetuses, weight differences between singletons and twins were absent at 0.76 and modest at 0.87 gestation, underscoring the fact that twins belong to normality in fetal sheep not constituting a major malnutritive condition. Mean fetal blood pressure (FBP) of all fetuses was negatively correlated to fetal weight at 0.76 but not 0.87 gestation (P < 0.05). At this age, FBP and baroreceptor reflex sensitivity were increased in LW fetuses (P < 0.05), suggesting increased sympathetic activity and immaturity of circulatory control. Development of vagal modulation of fetal heart rate depended on fetal weight (P < 0.01). These functional associations were largely independent of twin pregnancies. We conclude, low fetal weight within the normal weight span is accompanied by a different trajectory of development of sympathetic blood pressure and vagal heart rate control. This may contribute to the development of elevated blood pressure in later life. Examination of the underlying mechanisms and consequences may contribute to the understanding of programming of cardiovascular diseases.

Heart rate variability analysis allows early asphyxia detection in ovine fetus.

Fetal heart rate (FHR) monitoring is commonly used to predict asphyxia but clinical and experimental studies have questioned its diagnostic value. We examined the usefulness of fetal heart rate variability (fHRV) measures in detecting early asphyxia using chronically instrumented fetal sheep under normoxic (n = 6) and asphyxic conditions (3 umbilical cord occlusions, n = 6). The occlusions consistently led to pH decreases from 7.35 + 0.01 to 7.09 + 0.03 ( P < .05). FHR showed biphasic deceleration during each occlusion, associated with increasing arterial blood pressure ( P < .05). RMSSD, an index of vagal modulation of fHRV, increased consistently during repeated occlusion induced FHR decelerations ( P < .05). Under normoxic conditions, RMSSD did not change during FHR decelerations and decreased during FHR accelerations ( P < .05). Our results suggest that an increase of RMSSD in association with FHR decelerations reflects initial vagal activation during fetal asphyxia. RMSSD may accurately identify asphyxic fetuses early. Clinical validation is needed.

On the rhythmicity of quadratic phase coupling in the tracé alternant EEG in healthy neonates

The time-variant quadratic phase coupling (QPC) in trace alternant (TA) EEG patterns in healthy full-term neonates (quiet sleep) was investigated by means of time-variant bispectral analysis. The frequency plain 1-1.5 Hz <=> 3.5-4.5 Hz was used as the region-of-interest. QPC rhythms with a frequency of approximately 0.1 Hz were found in all neonates (n = 6). It can be demonstrated that the QPC rhythm of the TA is generated by a pattern-spanning time-variant phase-locking process characterising early functional interactions in the immature brain.

Topographic analysis of engagement and disengagement of neural oscillators in photic driving: a combined electroencephalogram/magnetoencephalogram study.

Objective A coupled system of nonlinear neural oscillators with an individual resonance frequency is assumed to form the neuronal substrate for the photic driving phenomenon. The aim was to investigate the spatiotemporal stability of these oscillators and quantify the spatiotemporal process of engagement and disengagement of the neuronal oscillators in both multitrial and single-trial data. Methods White light-emitting diode flicker stimulation was used at 15 frequencies, which were set relative to the individual &agr; frequency of each of the 10 healthy participants. Simultaneously, the electroencephalogram (EEG) and the magnetoencephalogram (MEG) were recorded. Subsequently, spatiotemporal matching pursuit (MP) algorithms were used to analyze the EEG and MEG topographies. Results Intraindividually similar topographies were found at stimulation frequencies close to (1) the individual &agr; frequency and (2) half the individual &agr; frequency in the multitrial and the single-trial cases. In both stimulation frequency ranges, the authors observed stable topographies 5 to 10 stimuli after the beginning of the stimulation and lasting three nonexisting periods after the end of the stimulation. This was interpreted as the engaging/disengaging effect of the observed oscillations, because especially the frequency parameter adopted before and after stable topographies were observed. Topographic entrainment was slightly more pronounced in MEG as compared with that in EEG. Conclusions The results support the hypothesis of nonlinear information processing in human visual system, which can be described by nonlinear neural oscillators.

Detection of directed information flow in biosignals

Abstract Several analysis techniques have been developed for time series to detect interactions in multidimensional dynamic systems. When analyzing biosignals generated by unknown dynamic systems, awareness of the different concepts upon which these analysis techniques are based, as well as the particular aspects the methods focus on, is a basic requirement for drawing reliable conclusions. For this purpose, we compare four different techniques for linear time series analysis. In general, these techniques detect the presence of interactions, as well as the directions of information flow, in a multidimensional system. We review the different conceptual properties of partial coherence, a Granger causality index, directed transfer function, and partial directed coherence. The performance of these tools is demonstrated by application to linear dynamic systems.

Coordination of the EEG and the heart rate of preterm neonates during quiet sleep

Aim of this study was to confirm that EEG bursts are associated with heart rate (HR) accelerations, and to investigate the synchronicity between quadratic phase couplings (QPC) courses of the EEG and HR before and during burst activity during quiet sleep in preterm newborns. The time-courses of QPC between frequency components of the EEG ([0.25-1.0 Hz]<-->[4.0-6.0 Hz]) as well as between the Mayer-Traube-Hering (MTH) wave and the frequency component of the HR associated to the respiratory sinus arrhythmia (RSA) ([0.02-0.15 Hz]<-->[0.4-1.5 Hz]) were investigated in five preterm neonates. During quiet sleep, the EEG alternates between burst and interburst activity. The burst onsets were used to trigger an averaging procedure for the EEG, HR, and QPC courses. It can be demonstrated that the envelopes of the EEG rise after the burst onset accompanied by an acceleration of HR before or at the burst maximum. The QPC courses show that the HRs QPC increases before or at the burst onset whereas the increase of the EEGs QPC is delayed. The synchronous changes of EEG and HR as well as of the corresponding QPC courses indicate a coupling between cortical, thalamocortical and neurovegetative brain structures. Such a coupling might be mediated by the MTH waves in the blood pressure.

Nonlinear analysis and modeling of cortical activation and deactivation patterns in the immature fetal electrocorticogram

An approach combining time-continuous nonlinear stability analysis and a parametric bispectral method was introduced to better describe cortical activation and deactivation patterns in the immature fetal electroencephalogram (EEG). Signal models and data-driven investigations were performed to find optimal parameters of the nonlinear methods and to confirm the occurrence of nonlinear sections in the fetal EEG. The resulting measures were applied to the in utero electrocorticogram (ECoG) of fetal sheep at 0.7 gestation when organized sleep states were not developed and compared to previous results at 0.9 gestation. Cycling of the nonlinear stability of the fetal ECoG occurred already at this early gestational age, suggesting the presence of premature sleep states. This was accompanied by cycling of the time-variant biamplitude which reflected ECoG synchronization effects during premature sleep states associated with nonrapid eye movement sleep later in gestation. Thus, the combined nonlinear and time-variant approach was able to provide important insights into the properties of the immature fetal ECoG.

Analysis of time-variant quadratic phase couplings in the tracé alternant EEG by recursive estimation of 3rd-order time-frequency distributions.

The quantification of transient quadratic phase couplings (QPC) by means of time-variant bispectral analysis is a useful approach to explain several interrelations between signal components. A generalized recursive estimation approach for 3rd-order time-frequency distributions (3rd-order TFD) is introduced. Based on 3rd-order TFD, time-variant estimations of biamplitude (BA), bicoherence (BC) and phase bicoherence (PBC) can be derived. Different smoothing windows and local moment functions for an optimization of the estimation properties are investigated and compared. The methods are applied to signal simulations and EEG signals, and it can be shown that the new time-variant bispectral analysis results in a reliable quantification of QPC in the tracé alternant EEG of healthy neonates.