Association between prenatal glucocorticoid exposure and adolescent neurodevelopment: An observational follow-up study.

INTRODUCTION: Prenatal exposure to supraphysiological glucocorticoid (GC) levels may lead to long-lasting developmental changes in numerous biological systems. Our prior study identified an association between prenatal GC prophylaxis and reduced cognitive performance, electrocortical changes, and altered autonomic nervous system (ANS) activity in children aged 8-9 years. This follow-up study aimed to examine whether these findings persisted into adolescence. MATERIAL AND METHODS: Prospective observational follow-up study involving twenty-one 14- to 15-year-old adolescents born to mothers who received betamethasone for induction of fetal lung maturation in threatened preterm birth, but who were born with a normal weight appropriate for their gestational age (median 37+4 gestational weeks). Thirty-five children not exposed to betamethasone served as the reference group (median 37+6 gestational weeks). The primary endpoint was cognitive performance, measured by intelligence quotient (IQ). Key secondary endpoints included symptoms of attention-deficit/hyperactivity disorder (ADHD) and metabolic markers. Additionally, we determined electrocortical (electroencephalogram), hypothalamus-pituitary-adrenal axis (HPAA), and ANS activity in response to a standardized stress paradigm. RESULTS: No statistically significant group difference was observed in global IQ (adjusted mean: betamethasone 103.9 versus references 105.9, mean difference -2.0, 95% confidence interval [CI]: -7.12 to 3.12, p = 0.44). Similarly, ADHD symptoms, metabolic markers, the overall and stress-induced activity of the HPAA and the ANS did not differ significantly between groups. However, the betamethasone group exhibited reduced electrocortical activity in the frontal brain region (spectral edge frequency-adjusted means: 16.0 Hz versus 17.8 Hz, mean difference -1.83 Hz, 95% CI: -3.21 to -0.45, p = 0.01). CONCLUSIONS: In 14- to 15-year-old adolescents, prenatal GC exposure was not associated with differences in IQ scores or ANS activity compared to unexposed controls. However, decelerated electrocortical activity in the frontal region potentially reflects disturbances in the maturation of cortical and/or subcortical brain structures. The clinical significance of these changes remains unknown. Given the small sample size, selective participation/loss of follow-up and potential residual confounding, these findings should be interpreted cautiously. Further research is required to replicate these results in larger cohorts before drawing firm clinical conclusions.

Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy.

Objective.While it is well-known that epilepsy has a clear impact on the activity of both the central nervous system (CNS) and the autonomic nervous system (ANS), its role on the complex interplay between CNS and ANS has not been fully elucidated yet. In this work, pairwise and higher-order predictability measures based on the concepts of Granger Causality (GC) and partial information decomposition (PID) were applied on time series of electroencephalographic (EEG) brain wave amplitude and heart rate variability (HRV) in order to investigate directed brain-heart interactions associated with the occurrence of focal epilepsy.Approach.HRV and the envelopes ofδandαEEG activity recorded from ipsilateral (ipsi-EEG) and contralateral (contra-EEG) scalp regions were analyzed in 18 children suffering from temporal lobe epilepsy monitored during pre-ictal, ictal and post-ictal periods. After linear parametric model identification, we compared pairwise GC measures computed between HRV and a single EEG component with PID measures quantifying the unique, redundant and synergistic information transferred from ipsi-EEG and contra-EEG to HRV.Main results.The analysis of GC revealed a dominance of the information transfer from EEG to HRV and negligible transfer from HRV to EEG, suggesting that CNS activities drive the ANS modulation of the heart rhythm, but did not evidence clear differences betweenδandαrhythms, ipsi-EEG and contra-EEG, or pre- and post-ictal periods. On the contrary, PID revealed that epileptic seizures induce a reorganization of the interactions from brain to heart, as the unique predictability of HRV originated from the ipsi-EEG for theδwaves and from the contra-EEG for theαwaves in the pre-ictal phase, while these patterns were reversed after the seizure.Significance.These results highlight the importance of considering higher-order interactions elicited by PID for the study of the neuro-autonomic effects of focal epilepsy, and may have neurophysiological and clinical implications.

Association between antenatal glucocorticoid exposure and the activity of the stress system, cognition, and behavior in 8- to 9-year-old children: A prospective observational study.

INTRODUCTION: Glucocorticoid (GC) -induced fetal programming of the activity of the hypothalamus-pituitary-adrenal axis (HPAA) and its associated cognitive and behavioral consequences in later life have been well characterized in several animal species. However, information on humans is scarce. In this study, we examined HPAA activity markers and associated outcomes at 8 to 9 years of age among children prenatally exposed to GC for suspected preterm birth. Our hypothesis was that antenatal exposure to the betamethasone (BM) is associated with exacerbation of HPAA activity in childhood. MATERIAL AND METHODS: Prospective observational study in 31 children whose mothers received single (n = 19) or multiple (n = 12) courses of BM for threatened preterm birth but born with normal weight appropriate for the gestational age (median 37+6  weeks of gestation) compared with 38 non-exposed, age-matched children. Primary end point was the activity of the HPAA in response to the Trier Social Stress Test. Secondary end points were changes in autonomic nervous system (ANS) activity, cognitive performance (IQ), attention-deficit/hyperactivity disorder (ADHD) symptoms, and electrocortical activity (EEG). RESULTS: There was no statistically significant difference in HPAA activity markers between antenatal BM exposed and unexposed groups. ANS activity in BM-exposed children shifted towards a higher parasympathetic tone reflected by a higher overall high-frequency band power of heart rate variability. IQ scores were within normal limits for both groups; however, BM-exposed children had lower IQ scores than the unexposed group. BM-exposed group had marginally more ADHD core symptoms and increased electrocortical activity in the occipital brain region compared with controls. A monotonic dose-response relation between BM exposure and activity of the ANS and IQ was estimated in post-hoc analyses. CONCLUSIONS: Antenatal exposure to BM in the context of threatened preterm birth was not associated with changes in HPAA activity in childhood. However, BM exposure may be associated with changes in ANS activity. Antenatal GC prophylaxis is a valuable and often life-saving therapy, but its prescription may warrant a well-balanced risk-benefit assessment.

Influence of Individual Heart Rate on Nonlinear Brain-Heart Interactions Estimated by Convergent Cross Mapping in Schizophrenic Patients and Healthy Controls.

Quantification of directed (nonlinear) brain-heart interactions has turned to be an emerging topic of research and is important for the better understanding of central autonomic processing during specific diseases such as schizophrenia. Convergent Cross Mapping (CCM) was able to provide directed, frequency-selective and topographic views on existent interaction pattern of those patients. Investigations of the influence of individual heart rate (HR) on CCM estimations may further contribute to this topic. Relationship of mean HR and CCM was analyzed in a group of schizophrenic patients (N=17) and healthy controls (N=21). Influence of individual HR values was most pronounced for patients, for interactions from brain to heart and for the subgroup of patients with highest mean HR values.

Brain-heart interactions considering complex physiological data: processing schemes for time-variant, frequency-dependent, topographical and statistical examination of directed interactions by convergent cross mapping.

BACKGROUND: A multitude of complex methods is available to quantify interactions in highly complex physiological systems. Brain-heart interactions play an important role in identifying couplings between the central nervous system and the autonomic nervous system during defined physiological states or specific diseases. The crucial point of those interaction analyses is adequate pre-processing taking into account nonlinearity of data, intuitive graphical representation and suitable statistical evaluation of the achieved results. OBJECTIVE: The aim of this study is to provide generalized processing schemes for such investigations taking into account pre-processing, graphical representation and statistical analysis. APPROACH: Two defined data sets were used to develop these processing schemes. Brain-heart interactions in children with temporal lobe epilepsy during the pre-ictal, ictal and post-ictal periods as well as in patients with paranoid schizophrenia and healthy control subjects during the resting state period were investigated by nonlinear convergent cross mapping (CCM). Surrogate data, bootstrapping and linear mixed-effects model approaches were utilized for statistical analyses. MAIN RESULTS: CCM was able to reveal specific and statistically significant time- and frequency-dependent patterns of brain-heart interactions for children with temporal lobe epilepsy and provide a statistically significant pattern of topographic- and frequency-dependent brain-heart interactions for schizophrenic patients, as well as to show the differences from healthy control subjects. Suitable statistical models were found to quantify group differences. SIGNIFICANCE: Generalized processing schemes and crucial points of pre-processing, adapted interaction analysis and performed statistical analysis are provided. The general concept of analyses is transferable also to other methods of interactions analysis and data representing even more complex physiological systems.

Nonlinear Interaction Analysis of Cardiovascular-Respiratory Data by Means of Convergent Cross Mapping.

Appropriate analyses of directed complex interactions within the cardiovascular-respiratory system are of growing interest for a better understanding of physiological regulatory mechanisms in healthy subjects and diseased persons. There are various concepts to analyze such interactions. Convergent Cross Mapping (CCM) provides the possibility to define directed interactions in terms of nonlinear stability. A proof-of-principle approach is introduced to apply CCM to cardiovascular-respiratory data of healthy subjects during resting state period. Showing group results of time-invariant as well as single subject results of interval-based CCM, the introduced approach was able to quantify correct directionality and strength of interactions within the cardiovascular-respiratory system and to provide statistical thresholds for significant interactions. These results may serve as a methodological base to compare healthy subjects and diseased persons.

Cortical Correlates of Human Balance Control.

Balance control is a fundamental component of human every day motor activities such as standing or walking, and its impairment is associated with an increased risk of falling. However, in humans the exact neurobiological mechanisms underlying balance control are still unclear. Specifically, although previous studies have identified a number of cortical regions that become significantly activated during real or imagined balancing, the interactions within and between the relevant cortical regions remain to be investigated. The working hypothesis of this study is that cortical networks contribute to an optimization of balance control, and that this contribution can be revealed by partial directed coherence-a time-variant, frequency-selective and directed functional connectivity analysis tool. Electroencephalographic activity was recorded in 37 subjects during single-leg balancing on a stable as well as an unstable surface. Results of this study show that in the transition from balancing on a stable surface to an unstable surface, two topographically delimitable connectivity networks (weighted directed networks) are established; one associated with the alpha and one with the theta frequency band. The theta network sequence can be described as a set of subnetworks (modules) comprising the frontal, central and parietal cortex with individual temporal and spatial developments within and between those modules. In the alpha network, the occipital electrodes O1 and O2 act as a source, and the interactions propagate predominantly in the directions from occipital to parietal and to centro-parietal areas. These important findings indicate that balance control is supported by at least two functional cortical networks.

Rod Driven Frequency Entrainment and Resonance Phenomena.

A controversy exists on photic driving in the human visual cortex evoked by intermittent photic stimulation. Frequency entrainment and resonance phenomena are reported for frequencies higher than 12 Hz in some studies while missing in others. We hypothesized that this might be due to different experimental conditions, since both high and low intensity light stimulation were used. However, most studies do not report radiometric measurements, which makes it impossible to categorize the stimulation according to photopic, mesopic, and scotopic vision. Low intensity light stimulation might lead to scotopic vision, where rod perception dominates. In this study, we investigated photic driving for rod-dominated visual input under scotopic conditions. Twelve healthy volunteers were stimulated with low intensity light flashes at 20 stimulation frequencies, leading to rod activation only. The frequencies were multiples of the individual alpha frequency (α) of each volunteer in the range from 0.40 to 2.30(∗)α. Three hundred and six-channel whole head magnetoencephalography recordings were analyzed in time, frequency, and spatiotemporal domains with the Topographic Matching Pursuit algorithm. We found resonance phenomena and frequency entrainment for stimulations at or close to the individual alpha frequency (0.90-1.10(∗)α) and half of the alpha frequency (0.40-0.55(∗)α). No signs of resonance and frequency entrainment phenomena were revealed around 2.00(∗)α. Instead, on-responses at the beginning and off-responses at the end of each stimulation train were observed for the first time in a photic driving experiment at frequencies of 1.30-2.30(∗)α, indicating that the flicker fusion threshold was reached. All results, the resonance and entrainment as well as the fusion effects, provide evidence for rod-dominated photic driving in the visual cortex.

Discussion of "Computational Electrocardiography: Revisiting Holter ECG Monitoring".

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Computational Electrocardiography: Revisiting Holter ECG Monitoring" written by Thomas M. Deserno and Nikolaus Marx. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Deserno and Marx. In subsequent issues the discussion can continue through letters to the editor.

Nonlinear Directed Interactions Between HRV and EEG Activity in Children With TLE.

OBJECTIVE: Epileptic seizure activity influences the autonomic nervous system (ANS) in different ways. Heart rate variability (HRV) is used as indicator for alterations of the ANS. It was shown that linear, nondirected interactions between HRV and EEG activity before, during, and after epileptic seizure occur. Accordingly, investigations of directed nonlinear interactions are logical steps to provide, e.g., deeper insight into the development of seizure onsets. METHODS: Convergent cross mapping (CCM) investigates nonlinear, directed interactions between time series by using nonlinear state space reconstruction. CCM is applied to simulated and clinically relevant data, i.e., interactions between HRV and specific EEG components of children with temporal lobe epilepsy (TLE). In addition, time-variant multivariate Autoregressive model (AR)-based estimation of partial directed coherence (PDC) was performed for the same data. RESULTS: Influence of estimation parameters and time-varying behavior of CCM estimation could be demonstrated by means of simulated data. AR-based estimation of PDC failed for the investigation of our clinical data. Time-varying interval-based application of CCM on these data revealed directed interactions between HRV and delta-related EEG activity. Interactions between HRV and alpha-related EEG activity were visible but less pronounced. EEG components mainly drive HRV. The interaction pattern and directionality clearly changed with onset of seizure. Statistical relevant interactions were quantified by bootstrapping and surrogate data approach. CONCLUSION AND SIGNIFICANCE: In contrast to AR-based estimation of PDC CCM was able to reveal time-courses and frequency-selective views of nonlinear interactions for the further understanding of complex interactions between the epileptic network and the ANS in children with TLE.

Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence.

Quantification of functional connectivity in physiological networks is frequently performed by means of time-variant partial directed coherence (tvPDC), based on time-variant multivariate autoregressive models. The principle advantage of tvPDC lies in the combination of directionality, time variance and frequency selectivity simultaneously, offering a more differentiated view into complex brain networks. Yet the advantages specific to tvPDC also cause a large number of results, leading to serious problems in interpretability. To counter this issue, we propose the decomposition of multi-dimensional tvPDC results into a sum of rank-1 outer products. This leads to a data condensation which enables an advanced interpretation of results. Furthermore it is thereby possible to uncover inherent interaction patterns of induced neuronal subsystems by limiting the decomposition to several relevant channels, while retaining the global influence determined by the preceding multivariate AR estimation and tvPDC calculation of the entire scalp. Finally a comparison between several subjects is considerably easier, as individual tvPDC results are summarized within a comprehensive model equipped with subject-specific loading coefficients. A proof-of-principle of the approach is provided by means of simulated data; EEG data of an experiment concerning visual evoked potentials are used to demonstrate the applicability to real data.

Matching Pursuit-Based Time-Variant Bispectral Analysis and its Application to Biomedical Signals.

OBJECTIVE: Principle aim of this study is to investigate the performance of a matching pursuit (MP)-based bispectral analysis in the detection and quantification of quadratic phase couplings (QPC) in biomedical signals. Nonlinear approaches such as time-variant bispectral analysis are able to provide information about phase relations between oscillatory signal components. METHODS: Time-variant QPC analysis is commonly performed using Gabor transform (GT) or Morlet wavelet transform (MWT), and is affected by either constant or frequency-dependent time-frequency resolution (TFR). The matched Gabor transform (MGT), which emerges from the incorporation of GT into MP, can overcome this obstacle by providing a complex time-frequency plane with an individually tailored TFR for each transient oscillatory component. QPC analysis was performed by MGT, and MWT was used as the state-of-the-art method for comparison. RESULTS: Results were demonstrated using simulated data, which present the general case of QPC, and biomedical benchmark data with a priori knowledge about specific signal components. HRV of children during temporal lobe epilepsy and EEG during burst-interburst pattern of neonates during quiet sleep were used for the biomedical signal analysis to investigate the two main areas of biomedical signal analysis: The cardiovascular-cardiorespiratory system and neurophysiological brain activities, respectively. Simulations were able to show the applicability and reliability of the MGT for bispectral analysis. HRV and EEG analysis demonstrate the general validity of the MGT for QPC detection by quantifying statistically significant time patterns of QPC. CONCLUSION AND SIGNIFICANCE: Results confirm that MGT-based bispectral analysis provides significant benefits for the analysis of QPC in biomedical signals.

Convergent Cross Mapping: Basic concept, influence of estimation parameters and practical application.

In neuroscience, data are typically generated from neural network activity. Complex interactions between measured time series are involved, and nothing or only little is known about the underlying dynamic system. Convergent Cross Mapping (CCM) provides the possibility to investigate nonlinear causal interactions between time series by using nonlinear state space reconstruction. Aim of this study is to investigate the general applicability, and to show potentials and limitation of CCM. Influence of estimation parameters could be demonstrated by means of simulated data, whereas interval-based application of CCM on real data could be adapted for the investigation of interactions between heart rate and specific EEG components of children with temporal lobe epilepsy.

Time-variant, frequency-selective, linear and nonlinear analysis of heart rate variability in children with temporal lobe epilepsy.

The major aim of our study is to demonstrate that a concerted combination of time-variant, frequency-selective, linear and nonlinear analysis approaches can be beneficially used for the analysis of heart rate variability (HRV) in epileptic patients to reveal premonitory information regarding an imminent seizure and to provide more information on the mechanisms leading to changes of the autonomic nervous system. The quest is to demonstrate that the combined approach gains new insights into specific short-term patterns in HRV during preictal, ictal, and postictal periods in epileptic children. The continuous Morlet-wavelet transform was used to explore the time-frequency characteristics of the HRV using spectrogram, phase-locking, band-power and quadratic phase coupling analyses. These results are completed by time-variant characteristics derived from a signal-adaptive approach. Advanced empirical mode decomposition was utilized to separate out certain HRV components, in particular blood-pressure-related Mayer waves (≈0.1 Hz) and respiratory sinus arrhythmia (≈0.3 Hz). Their time-variant nonlinear predictability was analyzed using local estimations of the largest Lyapunov exponent (point prediction error). Approximately 80-100 s before the seizure onset timing and coordination of both HRV components can be observed. A higher degree of synchronization is found and with it a higher predictability of the HRV. All investigated linear and nonlinear analyses contribute with a specific importance to these results.

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure.

Abstract An innovative concept for synchronization analysis between heart rate (HR) components and rhythms in EEG envelopes is represented; it applies time-variant analyses to heart rate variability (HRV) and EEG, and it was tested in children with temporal lobe epilepsy (TLE). After a removal of ocular and movement-related artifacts, EEG band activity was computed by means of the frequency-selective Hilbert transform providing envelopes of frequency bands. Synchronization between HRV and EEG envelopes was quantified by Morlet wavelet coherence. A surrogate data approach was adapted to test for statistical significance of time-variant coherences. Using this processing scheme, significant coherence values between a HRV low-frequency sub-band (0.08-0.12 Hz) and the EEG δ envelope (1.5-4 Hz) occurring both in the preictal and early postictal periods of a seizure can be shown. Investigations were performed for all electrodes at 20-s intervals and for selected electrode pairs (T3÷C3, T4÷C4) in a time-variant mode. Synchronization was more pronounced in the group of right hemispheric TLE patients than in the left hemispheric group. Such a group-specific augmentation of synchronization confirms the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the low-frequency HRV components.

Advantages of signal-adaptive approaches for the nonlinear, time-variant analysis of heart rate variability of children with temporal lobe epilepsy.

Major aim of our study is to demonstrate that signal-adaptive approaches improve the nonlinear and time-variant analysis of heart rate variability (HRV) of children with temporal lobe epilepsy (TLE). Nonlinear HRV analyses are frequently applied in epileptic patients. As HRV is characterized by components with oscillatory properties frequency-selective methods are in the focus, whereby application of nonlinear analysis to linear filtered signals seems to be doubtful. Signal-adaptive methods that preserve nonlinear properties and utilize only the signal data for an automatic computation of the result could benefit to nonlinear analysis of HRV. Combinations of (1) the signal-adaptive Matched Gabor Transform with time-variant nonlinear bispectral analysis and of (2) signal-adaptive Empirical Mode Decomposition methods with time-variant nonlinear stability analysis are investigated with regard to their application in the analysis of specific HRV components (respiratory sinus arrhythmia and Mayer wave associated low-frequency HRV components) of 18 children with TLE. Changes of timing and coordination of both HRV components during preictal, ictal and postictal periods occur which can be better quantified by advanced signal-adaptive methods. Both approaches contribute with specific importance to the analysis.

A processing scheme for time-variant phase analysis in EEG burst activity of premature and full-term newborns in quiet sleep: a methodological study.

A processing scheme for the investigation of neonatal electroencephalographic burst oscillations that is composed of time-variant methods for linear and nonlinear phase analysis is introduced. Starting from a time-frequency analysis of oscillations' amplitudes, time-variant approaches for quantification of phase locking, n:m phase synchronization, and quadratic phase coupling are applied. Tracé discontinue patterns from premature newborns and tracé alternant patterns from full-term newborns were investigated using bipolar EEG recordings. Maturation-related differences between the burst generation mechanisms can be shown, which are reflected in group-specific patterns of augmentation, timing, and grouping of time-varying phase characteristics of the EEG burst oscillations. We demonstrate for both groups (premature and full-term newborns) that phase-locked low-frequency oscillations are pronounced in the frequency range of 0.5-1.5 Hz. Phase-locked oscillations also occur in a frequency range of >3 Hz. The amplitude of a phase-locked 2-Hz oscillation is higher in full-term than in premature newborns. After onset, n:m synchronization and an increase in bicoherence occur earlier in the premature group (between 0.5-1.5 Hz and 3.0-6.0 Hz). It can be suggested that during the maturation process, the driving force of thalamic structures decreases and that cortical activity plays an increasingly important role in the process of burst generation.