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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.