Cerebral Autoregulation and Neurovascular Coupling after Craniospinal Irradiation in Long-Term Survivors of Malignant Pediatric Brain Tumors of the Posterior Fossa.

INTRODUCTION: Long-term survivors of craniospinal irradiation have an increased risk for stroke which increases with radiation dose and follow-up time. Radiotherapy induces structural changes of the cerebral vasculature, affecting both, large, and small vessels. It is unknown how these structural changes affect functional mechanisms of cerebral blood flow regulation such as cerebral autoregulation and neurovascular coupling. METHODS: Using the transcranial Doppler, we compared dynamic cerebral autoregulation and neurovascular coupling of 12 patients after long-term survival of craniospinal irradiation due to a malignant pediatric brain tumor of the posterior fossa and 12 age- and sex-matched healthy patients. Mean arterial blood pressure and cerebral blood flow velocities in the middle and posterior cerebral artery were recorded at rest during normal breathing to assess cerebral autoregulation (transfer function parameters phase and gain, as well as the correlation coefficient indices Mx, Sx, and Dx), and during 10 cycles of a visual task to assess neurovascular coupling (parameters time delay, natural frequency, gain, attenuation, and rate time). RESULTS: Parameters of cerebral autoregulation showed a consistent trend toward reduced cerebral autoregulation in patients that did not reach statistical significance. Neurovascular coupling was not altered after craniospinal irradiation. CONCLUSION: In this pilot study, we demonstrated a trend toward reduced cerebral autoregulation, and no alteration of neurovascular coupling after irradiation in long-term survivors of malignant pediatric brain tumors of the posterior fossa.

Quantitative assessment of cerebral connectivity deficiency and cognitive impairment in children with prenatal alcohol exposure.

It is common knowledge that alcohol consumption during pregnancy would cause cognitive impairment in children. However, recent works suggested that the risk of drinking during pregnancy may have been exaggerated. It is critical to determine whether and up to which amount the consumption of alcohol will affect the cognitive development of children. We evaluate time-varying functional connectivity using magnetoencephalogram data from somatosensory evoked response experiments for 19 teenage subjects with prenatal alcohol exposure and 21 healthy control teenage subjects using a new time-varying connectivity approach, combining renormalised partial directed coherence with state space modeling. Children exposed to alcohol prenatally are at risk of developing a Fetal Alcohol Spectrum Disorder (FASD) characterized by cerebral connectivity deficiency and impaired cognitive abilities. Through a comparison study of teenage subjects exposed to alcohol prenatally with healthy control subjects, we establish that the inter-hemispheric connectivity is deficient for the former, which may lead to disruption in the cortical inter-hemispheric connectivity and deficits in higher order cognitive functions as measured by an IQ test, for example. We provide quantitative evidence that the disruption is correlated with cognitive deficits. These findings could lead to a novel, highly sensitive biomarker for FASD and support a recommendation of no safe amount of alcohol consumption during pregnancy.

Inferring directed climatic interactions with renormalized partial directed coherence and directed partial correlation.

Inferring interactions between processes promises deeper insight into mechanisms underlying network phenomena. Renormalised partial directed coherence is a frequency-domain representation of the concept of Granger causality, while directed partial correlation is an alternative approach for quantifying Granger causality in the time domain. Both methodologies have been successfully applied to neurophysiological signals for detecting directed relationships. This paper introduces their application to climatological time series. We first discuss the application to El Niño-Southern Oscillation-Monsoon interaction and then apply the methodologies to the more challenging air-sea interaction in the South Atlantic Convergence Zone (SACZ). In the first case, the results obtained are fully consistent with the present knowledge in climate modeling, while in the second case, the results are, as expected, less clear, and to fully elucidate the SACZ air-sea interaction, further investigations on the specificity and sensitivity of these methodologies are needed.

Impaired dynamic cerebral autoregulation in patients with cerebral amyloid angiopathy.

Cerebral amyloid angiopathy (CAA) might disturb the sensitive mechanism of cerebral pressure autoregulation. This study examines whether dynamic cerebral autoregulation (CA) is impaired in the posterior or anterior circulation of CAA patients. Fifteen patients with known CAA on magnetic resonance imaging (MRI) and 14 age-matched controls were examined with transcranial Doppler. Dynamic CA was assessed in the middle (MCA) and posterior cerebral artery (PCA) via transfer function phase and gain during respiratory-induced 0.1 Hz oscillations of arterial pressure. Within the patient group, 4 patients showed additional microbleeds in the basal ganglia on the MRI performed within the study (pure CAA vs mixed microbleeds). PCA phase was significantly lower in patients compared with controls (p = 0.018), particularly in patients with pure CAA (p = 0.0034). MCA values showed a similar but non-significant trend towards lower phase in patients with pure CAA. Poorer phase was associated with a higher number of microbleeds on MRI (MCA r = -0.57, p = 0.027; PCA r = -0.52, p = 0.098) and superficial cortical siderosis (PCA: p = 0.0025). In conclusion, dynamic cerebral autoregulation is impaired in patients with CAA. The degree of impairment is associated with the extent of cerebral microbleeds.

Preeclampsia postpartum: Impairment of cerebral autoregulation and reversible cerebral hyperperfusion.

OBJECTIVES: Preeclampsia is a pregnancy-related hypertensive disorder with endothelial dysfunction. Impaired cerebral autoregulation may lead to symptomatic cerebral hyperperfusion, which sometimes manifests not until after delivery. This study investigated, whether cerebral autoregulation was altered after delivery in healthy and preeclamptic women, and whether this associated with cerebral hyperperfusion. STUDY DESIGN: In a prospective study, 35 preeclamptic and 35 healthy women were examined with transcranial Doppler within 10 days postpartum and 6 months later. Continuous arterial blood pressure and cerebral blood flow velocities (CBFV) in the middle (MCA) and posterior cerebral arteries (PCA) were recorded at rest. MAIN OUTCOME MEASURES: Dynamic cerebral autoregulation was assessed upon regular breathing at 0.1 Hz via transfer function phase and gain between arterial blood pressure and CBFV oscillations. RESULTS: In preeclamptic women, phase was reduced after delivery in both, MCA and PCA. During the postpartum period, CBFV of the MCA, but not PCA, correlated with higher arterial blood pressure and poorer dynamic cerebral autoregulation. In healthy women with only moderately altered cerebral autoregulation, CBFV remained in the normal range. At both measurements, arterial blood pressure was higher in preeclamptic compared to healthy women. CONCLUSIONS: Women with preeclampsia had poorer cerebral autoregulation and an increased risk of transient cerebral hyperperfusion after delivery.

Improving network inference: The impact of false positive and false negative conclusions about the presence or absence of links.

BACKGROUND: A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely on statistical inference, typically controlling the number of false positives, paying little attention to false negatives. NEW METHOD: In this paper, by means of a comprehensive simulation study, we analyse the influence of false positive and false negative conclusions about the presence or absence of links in a network on the network topology. We show that different values to balance false positive and false negative conclusions about links should be used in order to reliably estimate network characteristics. We propose to run careful simulation studies prior to making potentially erroneous conclusion about the network topology. RESULTS: Our analysis shows that optimal values to balance false positive and false negative conclusions about links depend on the network topology and characteristic of interest. COMPARISON WITH EXISTING METHODS: Existing methods rely on a choice of the rate for false positive conclusions. They aim to be sure about individual links rather than the entire network. The rate of false negative conclusions is typically not investigated. CONCLUSIONS: Our investigation shows that the balance of false positive and false negative conclusions about links in a network has to be tuned for any network topology that is to be estimated. Moreover, within the same network topology, the results are qualitatively the same for each network characteristic, but the actual values leading to reliable estimates of the characteristics are different.

Detection of time-, frequency- and direction-resolved communication within brain networks.

Electroencephalography (EEG) records fast-changing neuronal signalling and communication and thus can offer a deep understanding of cognitive processes. However, traditional data analyses which employ the Fast-Fourier Transform (FFT) have been of limited use as they do not allow time- and frequency-resolved tracking of brain activity and detection of directional connectivity. Here, we applied advanced qEEG tools using autoregressive (AR) modelling, alongside traditional approaches, to murine data sets from common research scenarios: (a) the effect of age on resting EEG; (b) drug actions on non-rapid eye movement (NREM) sleep EEG (pharmaco-EEG); and (c) dynamic EEG profiles during correct vs incorrect spontaneous alternation responses in the Y-maze. AR analyses of short data strips reliably detected age- and drug-induced spectral EEG changes, while renormalized partial directed coherence (rPDC) reported direction- and time-resolved connectivity dynamics in mice. Our approach allows for the first time inference of behaviour- and stage-dependent data in a time- and frequency-resolved manner, and offers insights into brain networks that underlie working memory processing beyond what can be achieved with traditional methods.

No long-term impairment of cerebral autoregulation after preeclampsia.

INTRODUCTION: Preeclampsia is a pregnancy-related hypertensive disorder with strongly impaired cerebral autoregulation in the acute stage. A history of preeclampsia is an independent cardiovascular and cerebrovascular risk factor. It is unclear whether impaired cerebral autoregulation persists after preeclampsia and thus contributes to the known increased cerebrovascular morbidity. METHODS: Using transcranial Doppler, we compared cerebral hemodynamics and dynamic cerebral autoregulation of 25 women with a history of severe preeclampsia and 25 healthy mothers, on average 2-3 years postpartum. Mean arterial blood pressure (MAP) and cerebral blood flow velocities (CBFV) in the middle and posterior cerebral artery were recorded at rest, dynamic cerebral autoregulation was assessed via transfer function phase and gain between oscillations of CBFV and MAP during regular breathing at 0.1 Hz. RESULTS: MAP and body mass index were higher in former preeclamptic women compared with healthy mothers (p-value <0.001 and 0.006, respectively). CBFV in the middle cerebral artery was slightly increased in former preeclamptic women compared with healthy mothers (p-value 0.004), intima-media thickness (IMT) of the common carotid artery was higher by trend (p-value 0.065). Dynamic cerebral autoregulation was not impaired in women with a history of preeclampsia, phase even tended to be higher than in healthy mothers. CONCLUSION: Dynamic cerebral autoregulation is not persistently impaired in women after severe preeclampsia. Long-term cerebrovascular changes rather result from a higher incidence of cerebrovascular risk factors in women with a history of preeclampsia.

Assessing the strength of directed influences among neural signals: an approach to noisy data.

BACKGROUND: Measurements in the neurosciences are afflicted with observational noise. Granger-causality inference typically does not take this effect into account. We demonstrate that this leads to false positives conclusions and spurious causalities. NEW METHOD: State space modelling provides a convenient framework to obtain reliable estimates for Granger-causality. Despite its previous application in several studies, the analytical derivation of the statistics for parameter estimation in the state space model was missing. This prevented a rigorous evaluation of the results. RESULTS: In this manuscript we derive the statistics for parameter estimation in the state space model. We demonstrate in an extensive simulation study that our novel approach outperforms standard approaches and avoids false positive conclusions about Granger-causality. COMPARISON WITH EXISTING METHODS: In comparison with the naive application of Granger-causality inference, we demonstrate the superiority of our novel approach. The wide-spread applicability of our procedure provides a statistical framework for future studies. The application to mice electroencephalogram data demonstrates the immediate applicability of our approach. CONCLUSIONS: The analytical derivation of the statistics presented in this manuscript enables a rigorous evaluation of the results of Granger causal network inference. It is noteworthy that the statistics can be readily applied to various measures for Granger causality and other approaches that are based on vector autoregressive models.

Granger causal time-dependent source connectivity in the somatosensory network.

Exploration of transient Granger causal interactions in neural sources of electrophysiological activities provides deeper insights into brain information processing mechanisms. However, the underlying neural patterns are confounded by time-dependent dynamics, non-stationarity and observational noise contamination. Here we investigate transient Granger causal interactions using source time-series of somatosensory evoked magnetoencephalographic (MEG) elicited by air puff stimulation of right index finger and recorded using 306-channel MEG from 21 healthy subjects. A new time-varying connectivity approach, combining renormalised partial directed coherence with state space modelling, is employed to estimate fast changing information flow among the sources. Source analysis confirmed that somatosensory evoked MEG was mainly generated from the contralateral primary somatosensory cortex (SI) and bilateral secondary somatosensory cortices (SII). Transient Granger causality shows a serial processing of somatosensory information, 1) from contralateral SI to contralateral SII, 2) from contralateral SI to ipsilateral SII, 3) from contralateral SII to contralateral SI, and 4) from contralateral SII to ipsilateral SII. These results are consistent with established anatomical connectivity between somatosensory regions and previous source modeling results, thereby providing empirical validation of the time-varying connectivity analysis. We argue that the suggested approach provides novel information regarding transient cortical dynamic connectivity, which previous approaches could not assess.

Optimized spectral estimation for nonlinear synchronizing systems.

In many fields of research nonlinear dynamical systems are investigated. When more than one process is measured, besides the distinct properties of the individual processes, their interactions are of interest. Often linear methods such as coherence are used for the analysis. The estimation of coherence can lead to false conclusions when applied without fulfilling several key assumptions. We introduce a data driven method to optimize the choice of the parameters for spectral estimation. Its applicability is demonstrated based on analytical calculations and exemplified in a simulation study. We complete our investigation with an application to nonlinear tremor signals in Parkinson's disease. In particular, we analyze electroencephalogram and electromyogram data.

Block-bootstrapping for noisy data.

BACKGROUND: Statistical inference of signals is key to understand fundamental processes in the neurosciences. It is essential to distinguish true from random effects. To this end, statistical concepts of confidence intervals, significance levels and hypothesis tests are employed. Bootstrap-based approaches complement the analytical approaches, replacing the latter whenever these are not possible. NEW METHOD: Block-bootstrap was introduced as an adaption of the ordinary bootstrap for serially correlated data. For block-bootstrap, the signals are cut into independent blocks, yielding independent samples. The key parameter for block-bootstrapping is the block length. In the presence of noise, naïve approaches to block-bootstrapping fail. Here, we present an approach based on block-bootstrapping which can cope even with high noise levels. This method naturally leads to an algorithm of block-bootstrapping that is immediately applicable to observed signals. RESULTS: While naïve block-bootstrapping easily results in a misestimation of the block length, and therefore in an over-estimation of the confidence bounds by 50%, our new approach provides an optimal determination of these, still keeping the coverage correct. COMPARISON WITH EXISTING METHODS: In several applications bootstrapping replaces analytical statistics. Block-bootstrapping is applied to serially correlated signals. Noise, ubiquitous in the neurosciences, is typically neglected. Our new approach not only explicitly includes the presence of (observational) noise in the statistics but also outperforms conventional methods and reduces the number of false-positive conclusions. CONCLUSIONS: The presence of noise has impacts on statistical inference. Our ready-to-apply method enables a rigorous statistical assessment based on block-bootstrapping for noisy serially correlated data.

Grading of dynamic cerebral autoregulation without blood pressure recordings: a simple Doppler-based method.

Transcranial Doppler sonography allows for noninvasive assessment of dynamic cerebral autoregulation. A wider clinical use of this approach has been hampered by the need for continuous arterial blood pressure (ABP) measurements. We describe a new method of a pure Doppler signal based estimation of dynamic autoregulation using heart rate (HR) and cerebral blood flow velocity (CBFV) information. The phase between these two signals was assessed from 0.1 Hz oscillations induced by regular breathing. We compared this new approach with the standard method (phase between ABP and CBFV oscillations) in 93 patients with unilateral severe carotid artery obstruction. On a group level, the phase HR-CBFV differed significantly between ipsi- and contralateral sides (p = 0.024) and correlated significantly with the standard phase ABP-CBFV (r = 0.369, p < 0.001). The proposed method can, thus, detect impaired dynamic autoregulation in occlusive carotid artery disease using a single Doppler probe.

Inference of Granger causal time-dependent influences in noisy multivariate time series.

Inferring Granger-causal interactions between processes promises deeper insights into mechanisms underlying network phenomena, e.g. in the neurosciences where the level of connectivity in neural networks is of particular interest. Renormalized partial directed coherence has been introduced as a means to investigate Granger causality in such multivariate systems. A major challenge in estimating respective coherences is a reliable parameter estimation of vector autoregressive processes. We discuss two shortcomings typical in relevant applications, i.e. non-stationarity of the processes generating the time series and contamination with observational noise. To overcome both, we present a new approach by combining renormalized partial directed coherence with state space modeling. A numerical efficient way to perform both the estimation as well as the statistical inference will be presented.

Multivariate analysis of dynamical processes with applications to the neurosciences.

Nowadays, data are recorded with increasing spatial and temporal resolution. Commonly these data are analyzed using univariate or bivariate approaches. Most of the analysis techniques assume stationarity of the underlying dynamical processes. Here, we present an approach that is capable of analyzing multivariate data, the so-called renormalized partial directed coherence. It utilizes the concept of Granger causality and is applicable to non-stationary data. We discuss its abilities and limitations, and demonstrate its usefulness in an application to murine electroencephalography (EEG) data during sleep transitions.

On the estimation of the direction of information flow in networks of dynamical systems.

The inference of the interaction structure in networks of dynamical systems promises novel insights into the functioning or malfunctioning of systems in the neurosciences. This may improve the understanding of mechanisms underlying several diseases like tremor disorders and might eventually help to cure patients. Of particular interest is the estimation of the direction of information flow for which different methods have been suggested and have been applied to data from human tremor. Based on a simulated system motivated by the human tremor application we analyze the performance of three methods. The abilities and limitations of the individual techniques are compared and discussed. An application to essential tremor complements this investigation.

Estimating causal dependencies in networks of nonlinear stochastic dynamical systems.

The inference of causal interaction structures in multivariate systems enables a deeper understanding of the investigated network. Analyzing nonlinear systems using partial directed coherence requires high model orders of the underlying vector-autoregressive process. We present a method to overcome the drawbacks caused by the high model orders. We calculate the corresponding statistics and provide a significance level. The performance is illustrated by means of model systems and in an application to neurological data.

Time-variant estimation of directed influences during Parkinsonian tremor.

The inference of interaction structures in multidimensional time series is a major challenge not only in neuroscience but in many fields of research. To gather information about the connectivity in a network from measured data, several parametric as well as non-parametric approaches have been proposed and widely examined. Today a lot of interest is focused on the evolution of the network connectivity in time which might contain information about ongoing tasks in the brain or possible dynamic dysfunctions. Therefore an extension of the current approaches towards time-resolved analysis techniques is desired. We present a parametric approach for time variant analysis, test its performance for simulated data, and apply it to real-world data.