Search

One of the mutations in the microtubule-associated protein tau, P301S, is causative for dominantly inherited frontotemporal dementia characterized by extensive tau pathology for which no licensed treatment is available. Hydromethylthionine is a potent tau aggregation inhibitor. We report treatment of an asymptomatic carrier of the P301S mutation using hydromethylthionine over a 5-year period beginning at the mean age of onset of clinical decline in the family. During the period of treatment, the rates of progression of cerebral atrophy were reduced by 61%–66% in frontal and temporal lobes, and the patient remained clinically asymptomatic.

Debates on six controversial topics on the network theory of epilepsy were held during two debate sessions, as part of the International Conference for Technology and Analysis of Seizures, 2019 (ICTALS 2019) convened at the University of Exeter, UK, September 2-5 2019. The debate topics were (1) From pathologic to physiologic: is the epileptic network part of an existing large-scale brain network? (2) Are micro scale recordings pertinent for defining the epileptic network? (3) From seconds to years: do we need all temporal scales to define an epileptic network? (4) Is it necessary to fully define the epileptic network to control it? (5) Is controlling seizures sufficient to control the epileptic network? (6) Does the epileptic network want to be controlled? This article, written by the organizing committee for the debate sessions and the debaters, summarizes the arguments presented during the debates on these six topics.

Crossed cerebellar diaschisis (CCD) is characterized by hypometabolism and hypoperfusion on molecular imaging in the cerebellum due to a supratentorial lesion on the contralateral side. CCD is a well-established phenomenon in acute or subacute conditions such as infarction but it has been less well described in chronic conditions such as neurodegenerative dementias. Here, we investigate CCD in a large sample of 830 people meeting research criteria for Alzheimer's disease (AD) using [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET).This study is based on FDG-PET data collected at baseline as part of two large-scale Phase III clinical trials of a novel tau aggregation inhibitor medication, methylthioninium in mild to moderate AD participants. Quantification of FDG-PET hypometabolism was carried out using standardized uptake value ratio (SUVR), with the pons as the comparison region. SUVR was compared in different regions of interest between the right and left hemispheres of the brain and cerebellum in people with mild AD (Mini-Mental State Examination score ≥ 20).Comparison of SUVR in different brain regions demonstrated significant differences in the temporal, occipital and cerebellar cortices. Right and left asymmetry was noted with lower SUVR in the left temporal and occipital regions, whereas SUVR was lower in the right side of the cerebellum.Here, we found robust evidence of CCD in a large sample of people with AD, a chronic neurodegenerative condition. The presence of this phenomenon in AD opens up a new avenue of research in AD pathogenesis and has the potential to change future diagnostic and therapeutic strategies.

© Copyright © 2021 Deschle, Ignacio Gossn, Tewarie, Schelter and Daffertshofer.Modeling the dynamics of neural masses is a common approach in the study of neural populations. Various models have been proven useful to describe a plenitude of empirical observations including self-sustained local oscillations and patterns of distant synchronization. We discuss the extent to which mass models really resemble the mean dynamics of a neural population. In particular, we question the validity of neural mass models if the population under study comprises a mixture of excitatory and inhibitory neurons that are densely (inter-)connected. Starting from a network of noisy leaky integrate-and-fire neurons, we formulated two different population dynamics that both fall into the category of seminal Freeman neural mass models. The derivations contained several mean-field assumptions and time scale separation(s) between membrane and synapse dynamics. Our comparison of these neural mass models with the averaged dynamics of the population reveals bounds in the fraction of excitatory/inhibitory neuron as well as overall network degree for a mass model to provide adequate estimates. For substantial parameter ranges, our models fail to mimic the neural network's dynamics proper, be that in de-synchronized or in (high-frequency) synchronized states. Only around the onset of low-frequency synchronization our models provide proper estimates of the mean potential dynamics. While this shows their potential for, e.g., studying resting state dynamics obtained by encephalography with focus on the transition region, we must accept that predicting the more general dynamic outcome of a neural network via its mass dynamics requires great care.

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.

Background: LMTM is being developed as a treatment for AD based on inhibition of tau aggregation. Objectives: To examine the efficacy of LMTM as monotherapy in non-randomized cohort analyses as modified primary outcomes in an 18-month Phase III trial in mild AD. Methods: Mild AD patients (n = 800) were randomly assigned to 100 mg twice a day or 4 mg twice a day. Prior to unblinding, the Statistical Analysis Plan was revised to compare the 100 mg twice a day as monotherapy subgroup (n = 79) versus 4 mg twice a day as randomized (n = 396), and 4 mg twice a day as monotherapy (n = 76) versus 4 mg twice a day as add-on therapy (n = 297), with strong control of family-wise type I error. Results: The revised analyses were statistically significant at the required threshold of p

Background Hydromethylthionine is a potent inhibitor of pathological aggregation of tau and TDP-43 proteins. Objective To compare hydromethylthionine treatment effects at two doses and to determine how drug exposure is related to treatment response in bvFTD. Methods We undertook a 52-week Phase III study in 220 bvFTD patients randomized to compare hydromethylthionine at 200 mg/day and 8 mg/day (intended as a control). The principal outcomes were change on the Addenbrookes Cognitive Examination - Revised (ACE-R), the Functional Activities Questionnaire (FAQ), and whole brain volume. Secondary outcomes included Modified Clinical Global Impression of Change (Modified-CGIC). A population pharmacokinetic exposure-response analysis was undertaken in 175 of the patients with available blood samples and outcome data using a discriminatory plasma assay for the parent drug. Results There were no significant differences between the two doses as randomized. There were steep concentration-response relationships for plasma levels in the range 0.3-0.6 ng/ml at the 8 mg/day dose on clinical and MRI outcomes. There were significant exposure-dependent differences at 8 mg/day for FAQ, Modified-CGIC, and whole brain atrophy comparing patients with plasma levels greater than 0.346 ng/ml with having minimal drug exposure. The exposure-response is biphasic with worse outcomes at the high concentrations produced by 200 mg/day. Conclusions Hydromethylthionine has a similar concentration-response profile for effects on clinical decline and brain atrophy at the 8 mg/day dose in bvFTD as recently reported in AD. Treatment responses in bvFTD are predicted to be maximal at doses in the range 20-60 mg/day. A confirmatory placebo-controlled trial is now planned.

When a network is reconstructed from data, two types of errors can occur: false positive and false negative errors about the presence or absence of links. In this paper, the vertex degree distribution of the true underlying network is analytically reconstructed using an iterative procedure. Such procedure is based on the inferred network and estimates for the probabilities $\alpha$ and $\beta$ of type I and type II errors, respectively. The iteration procedure consists of choosing various values for $\alpha$ to perform the iteration steps of the network reconstruction. For the first step, the standard value for $\alpha$ of 0.05 can be chosen as an example. The result of this first step gives a first estimate of the network topology of interest. For the second iteration step the value for $\alpha$ is adjusted according to the findings of the first step. This procedure is iterated, ultimately leading to a reconstruction of the vertex degree distribution tailored to its previously unknown network topology.

Background Although hydromethylthionine is a potent tau aggregation inhibitor, no difference was found in either of two Phase III trials in mild to moderate Alzheimer's disease (AD) comparing doses in the range 150-250 mg/day with 8 mg/day intended as a control. Objective To determine how drug exposure is related to treatment response. Methods A sensitive plasma assay for the drug was used in a population pharmacokinetic analysis of samples from 1,162 of the 1,686 patients who participated in either of the Phase III trials with available samples and efficacy outcome data. Results There are steep concentration-response relationships for steady state plasma levels in the range 0.3-0.8 ng/ml at the 8 mg/day dose. Using a threshold based on the lower limit of quantitation of the assay on Day 1, there are highly significant differences in cognitive decline and brain atrophy in patients with above threshold plasma levels, both for monotherapy and add-on therapy, but with effect sizes reduced by half as add-on. Plasma concentrations in the range 4-21 ng/ml produced by the high doses are not associated with any additional benefit. Conclusions Hydromethylthionine has pharmacological activity on brain structure and function at the 8 mg/day dose as monotherapy or as add-on to symptomatic treatments. This combined with a plateau at higher doses is consistent with the lack of dose-response seen in the Phase III trials. Treatment benefit is predicted to be maximal at 16 mg/day as monotherapy. A placebo-controlled trial in mild/moderate AD is now ongoing to confirm efficacy at this dose.

When the network is reconstructed, two types of errors can occur: false positive and false negative errors about the presence or absence of links. In this paper, the influence of these two errors on the vertex degree distribution is analytically analysed. Moreover, an analytic formula of the density of the biased vertex degree distribution is found. In the inverse problem, we find a reliable procedure to reconstruct analytically the density of the vertex degree distribution of any network based on the inferred network and estimates for the false positive and false negative errors based on, e.g., simulation studies.

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.

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.

The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres: Hôpital Pitié-là-Salpêtrière, Paris, France; Universitätsklinikum Freiburg, Germany; Centro Hospitalar e Universitário de Coimbra, Portugal. For a considerable number of patients it was possible to find a patient specific predictor with an acceptable performance, as for example predictors that anticipate at least half of the seizures with a rate of false alarms of no more than 1 in 6 h (0.15 h⁻¹). We observed that the epileptic focus localization, data sampling frequency, testing duration, number of seizures in testing, type of machine learning, and preictal time influence significantly the prediction performance. The results allow to face optimistically the feasibility of a patient specific prospective alarming system, based on machine learning techniques by considering the combination of several univariate (single-channel) electroencephalogram features. We envisage that this work will serve as benchmark data that will be of valuable importance for future studies based on the European Epilepsy Database.

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.

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, naive 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 naive 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.

From the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly available in 2012. We illustrate its content and scope. The EPILEPSIAE database provides long-term EEG recordings of 275 patients as well as extensive metadata and standardized annotation of the data sets. It will adhere to the current standards in the field of prediction and facilitate reproducibility and comparison of those studies. Beyond seizure prediction, it may also be of considerable benefit for studies focusing on seizure detection, basic neurophysiology, and other fields.

Subclinical seizures (SCS) have rarely been considered in the diagnosis and therapy of epilepsy and have not been systematically analyzed in studies on seizure prediction. Here, we investigate whether predictions of subclinical seizures are feasible and how their occurrence may affect the performance of prediction algorithms. Using the European database of long-term recordings of surface and invasive electroencephalography data, we analyzed the data from 21 patients with SCS, including in total 413 clinically manifest seizures (CS) and 3341 SCS. Based on the mean phase coherence we investigated the predictive performance of CS and SCS. The two types of seizures had similar prediction sensitivities. Significant performance was found considerably more often for SCS than for CS, especially for patients with invasive recordings. When analyzing false alarms triggered by predicting CS, a significant number of these false predictions were followed by SCS for 9 of 21 patients. Although currently observed prediction performance may not be deemed sufficient for clinical applications for the majority of the patients, it can be concluded that the prediction of SCS is feasible on a similar level as for CS and allows a prediction of more of the seizures impairing patients, possibly also reducing the number of false alarms that were in fact correct predictions of CS. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Summary Purpose: In recent years, a variety of methods developed in the field of linear and nonlinear time series analysis have been used to obtain reliable predictions of epileptic seizures. Because individual methods for seizure prediction so far have shown statistical significance but insufficient performance for clinical applications, we investigated possible improvements by combining algorithms capturing different aspects of electroencephalogram (EEG) dynamics. Methods: We applied the mean phase coherence and the dynamic similarity index to long-term continuous intracranial EEG data. The predictive performance of both methods was assessed and statistically evaluated separately, as well as by using logical “AND” and “OR” combinations. Results: Used independently, either method resulted in a statistically significant prediction performance in only a few patients. Particularly the “AND” combination led to improved prediction performances, leading to an increase in sensitivity and/or specificity. For a maximum false prediction rate of 0.15/h, the mean sensitivity improved from about 25% for the individual methods to 43.2% for the “AND” and to 35.2% for the “OR” combination. Discussion: This study shows that combinations of prediction methods are promising new approaches to enhance seizure prediction performance considerably. It allows merging the individual benefits of prediction methods in a complementary manner. Because either sensitivity or specificity of seizure prediction methods can be improved depending on the needs of the desired clinical application, the combination opens a new window for future use in a clinical setting.

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.

To infer interactions from functional magnetic resonance imaging (fMRI) data, structural equation modeling (SEM) as well as dynamic causal modeling (DCM) has been suggested. Directed partial correlation (dPC) is a measure which detects Granger causality in multivariate systems. To demonstrate the strengths as well as the limitations of directed partial correlation we first applied it to simulated data tailored to the problem at hand. Second, after dPC has proven to be useful for fMRI data analysis, we applied it to actual fMRI data.

Objective: Retrospective evaluation and comparison of performances of a multivariate method for seizure detection and prediction on simultaneous long-term EEG recordings from scalp and intracranial electrodes. Methods: Two multivariate techniques based on simulated leaky integrate-and-fire neurons were investigated in order to detect and predict seizures. Both methods were applied and assessed on 423 h of EEG and 26 seizures in total, recorded simultaneously from the scalp and intracranially continuously over several days from six patients with pharmacorefractory epilepsy. Results: Features generated from simultaneous scalp and intracranial EEG data showed a similar dynamical behavior. Significant performances with sensitivities of up to 73%/62% for scalp/invasive EEG recordings given an upper limit of 0.15 false detections per hour were obtained. Up to 59%/50% of all seizures could be predicted from scalp/invasive EEG, given a maximum number of 0.15 false predictions per hour. A tendency to better performances for scalp EEG was obtained for the detection algorithm. Conclusions: The investigated methods originally developed for non-invasive EEG were successfully applied to intracranial EEG. Especially, concerning seizure detection the method shows a promising performance which is appropriate for practical applications in EEG monitoring. Concerning seizure prediction a significant prediction performance is indicated and a modification of the method is suggested. Significance: This study evaluates simultaneously recorded non-invasive and intracranial continuous long-term EEG data with respect to seizure detection and seizure prediction for the first time. � 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

In Nonlinear Dynamics synchronization of oscillators is examined. Alternatively for linear stochastic systems, coherence analysis is utilized to detect interdependencies in transfer function systems. In contrast to the latter, oscillators continue oscillating in the absence of interaction between the processes. For transfer function systems the output ceases to exist without an input. Analysis techniques able to differentiate these considerably different classes of dynamics are desired in various applications. We show that conclusions from analysis techniques to the underlying dynamics have to be taken with care due to missing specificity. Moreover, we present an approach towards higher specificity.

Determination of synchronization phenomena between pairs of coupled multivariate processes is of particular interest in Nonlinear Dynamics. Besides synchronization phenomena, coupling directions between the processes are investigated. We present an approach to analyze coupling directions in multivariate oscillatory stochastic systems. We propose usage of partial directed coherence developed in the framework of linear stochastic processes. We show that partial directed coherence is also applicable to detect coupling directions in nonlinear systems such as coupled stochastic van der Pol and stochastic Rössler systems. Furthermore, a differentiation between direct and indirect couplings in multivariate systems is possible when applying partial directed coherence.

Phase synchronization analysis is frequently applied to data originating for example from Physics and Life Sciences. Statistical properties for quantities measuring phase synchronization have not been revealed. We derive an analytic significance level for a frequently used phase synchronization measure. Its performance is demonstrated for a system of coupled oscillators.

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.

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.

In this Letter, we show that coherence and phase synchronization analysis are sensitive but not specific in detecting the correct class of underlying dynamics. We propose procedures to increase specificity and demonstrate the power of the approach by application to paradigmatic dynamic model systems. (c) 2006 Elsevier B.V. All rights reserved

Univariate and bivariate time series analysis techniques have enabled new insights into neural processes. However, these techniques are not feasible to distinguish direct and indirect interrelations in multivariate systems. To this aim multivariate times series techniques are presented and investigated by means of simulated as well as physiological time series. Pitfalls and limitations of these techniques are discussed.

One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity. When applying multivariate time series analysis techniques to neural signals, detection of directed relationships, which can be described in terms of Granger-causality, is of particular interest. Partial directed coherence has been introduced for a frequency domain analysis of linear Granger-causality based on modeling the underlying dynamics by vector autoregressive processes. We discuss the statistical properties of estimates for partial directed coherence and propose a significance level for testing for nonzero partial directed coherence at a given frequency. The performance of this test is illustrated by means of linear and non-linear model systems and in an application to electroencephalography and electromyography data recorded from a patient suffering from essential tremor.

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.

Objective : Coherence analysis of electromyography (EMG) signals in essential tremor (ET) suggests that tremor in the right and left arm is induced by independent central oscillators. The sensorimotor cortex seems to be part of the tremor-generating neuronal network in ET. Here, we investigated using electroencephalography (EEG) whether the independence of central oscillators in ET is supported by the analysis of cortical activity. Methods : In 8 patients with ET, bilateral hand tremor was activated by wrist extension. EMGs from the wrist flexors and extensors were recorded simultaneously with an EEG. EEG-EMG coherence was estimated for 74 epochs of 60 s duration. Results : In 42.6% of the cases, EEG-EMG coherence at the tremor frequency existed only with the contralateral sensorimotor cortex. However, 21.6% of the tremor-EMGs were coherent with EEG activity over both the contralateral and ipsilateral sensorimotor cortex. Bilateral and exclusively contralateral EEG-EMG coherence could alternate within the same recording. Bilateral EEG-EMG coherence was associated with increased right-left EEG-EEG coherence, increased right-left EMG-EMG coherence as well as with increased tremor strength. Conclusions : In ET, central oscillators in the right and left brain are not entirely independent of each other. They may dynamically synchronise, presumably by interhemispheric coupling via the corpus callosum.

Background Detecting causal interactions in multivariate systems, in terms of Granger-causality, is of major interest in the Neurosciences. Typically, it is almost impossible to observe all components of the system. Missing certain components can lead to the appearance of spurious interactions. The aim of this study is to demonstrate the effect of this and to demonstrate that distinction between latent confounders and volume conduction is possible in some cases. New method Our new method uses a combination of renormalised partial directed coherence and analysis of the (partial) covariance matrix of residual noise process to detect instantaneous, spurious interactions. Sub-network analyses are performed to infer the true network structure of the underlying system. Results We provide evidence that it is possible to distinguish between instantaneous interactions that occur as a result of a latent confounder and those that occur as a result of volume conduction. Comparison with existing methods Our novel approach demonstrates to what extent inference of unobserved important processes as well as the distinction between latent confounders and volume conduction is possible. We suggest a combination of measures of Granger-causality and covariance selection models to achieve this numerically. Conclusions Sub-network analyses enable a much more precise and correct inference of the true underlying network structure in some cases. From this it is possible to distinguish between unobserved processes and volume conduction. Our approach is straightforwardly adaptable to various measures of Granger-causality emphasising its ubiquitous successful applicability.

By now seizure prediction in epilepsy has a history of more than two decades. Several alternative approaches have been suggested to predict epileptic seizures. These approaches are mainly based on signal processing of electroencephalograms. In this chapter, the basic principles of signal processing geared to seizure prediction in the framework of epilepsy will be summarised and discussed.

In the analysis of neuroscience data, the identification of task-related causal relationships between various areas of the brain gives insights about the network of physiological pathways that are active during the task. One increasingly used approach to identify causal connectivity uses the concept of Granger causality that exploits predictability of activity in one region by past activity in other regions of the brain. Owing to the complexity of the data, selecting components for the analysis of causality as a preprocessing step has to be performed. This includes predetermined—and often arbitrary—exclusion of information. Therefore, the system is confounded by latent sources. In this paper, the effect of latent confounders is demonstrated, and paths of influence among three components are studied. While methods for analysing Granger causality are commonly based on linear vector autoregressive models, the effects of latent confounders are expected to be present also in nonlinear systems. Therefore, all analyses are also performed for a simulated nonlinear system and discussed with regard to applications in neuroscience.

International audience; From the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly available in 2012. We illustrate its content and scope. The EPILEPSIAE database provides long-term EEG recordings of 275 patients as well as extensive metadata and standardized annotation of the data sets. It will adhere to the current standards in the field of prediction and facilitate reproducibility and comparison of those studies. Beyond seizure prediction, it may also be of considerable benefit for studies focusing on seizure detection, basic neurophysiology, and other fields.

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.

The prediction of events is of substantial interest in many research areas. To evaluate the performance of prediction methods, the statistical validation of these methods is of utmost importance. Here, we compare an analytical validation method to numerical approaches that are based on Monte Carlo simulations. The comparison is performed in the field of the prediction of epileptic seizures. In contrast to the analytical validation method, we found that for numerical validation methods insufficient but realistic sample sizes can lead to invalid high rates of false positive conclusions. Hence we outline necessary preconditions for sound statistical tests on above chance predictions. Original Publication: Hinnerk Feldwisch-Drentrup, Andreas Schulze-Bonhage, Jens Timmer and Bjoern Schelter, Statistical validation of event predictors: A comparative study based on the field of seizure prediction, 2011, Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, (83), 6, 066704. http://dx.doi.org/10.1103/PhysRevE.83.066704 Copyright: American Physical Society http://www.aps.org/

A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community.

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.

A reliable algorithm for the timely prediction of epileptic seizures would be a milestone in epilepsy research. Prediction performances have so far been determined using retrospective data assessment, leaving open the question as to whether they prove statistically significant and clinically useful under prospective conditions. To this aim, a Seizure Prediction Competition has been set up. Here, the background and the details of this competition are described.

We propose a method to infer the coupling structure in networks of nonlinear oscillatory systems with multiple time scales. The method of partial phase synchronization allows us to infer the coupling structure for coupled nonlinear oscillators with one well-defined time scale. The case of oscillators with multiple time scales has remained a challenge until now. Here, we introduce partial recurrence based synchronization analysis to tackle this challenge. We successfully apply the proposed method to model systems and experimental data from coupled electrochemical oscillators. The statistical significance of the results is evaluated based on a surrogate hypothesis test.

Estimating the functional topology of a network from multivariate observations is an important task in nonlinear dynamics. We introduce the nonparametric partial directed coherence that allows disentanglement of direct and indirect connections and their directions. We illustrate the performance of the nonparametric partial directed coherence by means of a simulation with data from synchronized nonlinear oscillators and apply it to real-world data from a patient suffering from essential tremor.

Partial directed coherence is a powerful tool used to analyze interdependencies in multivariate systems based on vector autoregressive modeling. This frequency domain measure for granger-causality is designed such that it is normalized to [0,1]. This normalization induces several pitfalls for the interpretability of the ordinary partial directed coherence, which will be discussed in some detail in this paper. In order to avoid these pitfalls, we introduce renormalized partial directed coherence and calculate confidence intervals and significance levels. The performance of this novel concept is illustrated by application to model systems and to electroencephalography and electromyography data from a patient suffering from parkinsonian tremor.