Björn Schelter

Testing for directed influences among neural signals using partial directed coherence

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.

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

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

Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that prediction methods are applied to multiple time series and several seizures. Their performance is illustrated utilizing a bivariate seizure prediction method based on synchronization theory.

Assessing the strength of directed influences among neural signals using renormalized partial directed coherence

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.

Do False Predictions of Seizures Depend on the State of Vigilance? A Report from Two Seizure-Prediction Methods and Proposed Remedies

Summary:  Purpose: Available seizure‐prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep–wake cycle.

Joining the benefits: Combining epileptic seizure prediction methods

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.

Application of a multivariate seizure detection and prediction method to non-invasive and intracranial long-term EEG recordings

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 423h 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.

Spatio-temporal patient–individual assessment of synchronization changes for epileptic seizure prediction

OBJECTIVE Abnormal synchronization of neurons plays a central role for the generation of epileptic seizures. Therefore, multivariate time series analysis techniques investigating relationships between the dynamics of different neural populations may offer advantages in predicting epileptic seizures. METHODS We applied a phase and a lag synchronization measure to a selected subset of multicontact intracranial EEG recordings and assessed changes in synchronization with respect to seizure prediction. RESULTS Patient individual results, group results, spatial aspects using focal and extra-focal electrode contacts as well as two evaluation schemes analyzing decreases and increases in synchronization were examined. Averaged sensitivity values of 60% are observed for a false prediction rate of 0.15 false predictions per hour, a seizure occurrence period of half an hour, and a prediction horizon of 10 min. For approximately half of all 21 patients, a statistically significant prediction performance is observed for at least one synchronization measure and evaluation scheme. CONCLUSIONS The results indicate that synchronization changes in the EEG dynamics preceding seizures can be used for seizure prediction. Nevertheless, the underlying pathogenic mechanisms differ and both decreases and increases in synchronization may precede epileptic seizures depending on the structures investigated. SIGNIFICANCE The prediction method, optimized values of intervention times, as well as preferred brain structures for the EEG recordings have to be determined for each patient individually offering the chance of a better patient-individual prediction performance.

Dynamic synchronisation of central oscillators in essential tremor

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.

Tremor-correlated neuronal activity in the subthalamic nucleus of Parkinsonian patients

Tremor in Parkinsons disease (PD) is generated by an oscillatory neuronal network consisting of cortex, basal ganglia and thalamus. The subthalamic nucleus (STN) which is part of the basal ganglia is of particular interest, since deep brain stimulation of the STN is an effective treatment for PD including Parkinsonian tremor. It is controversial if and how the STN contributes to tremor generation. In this study, we analyze neuronal STN activity in seven patients with Parkinsonian rest tremor who underwent stereotactic surgery for deep brain stimulation. Surface EMG was recorded from the wrist flexors and extensors. Simultaneously, neuronal spike activity was registered in different depths of the STN using an array of five microelectrodes. After spike-sorting, spectral coherence was analyzed between spike activity of STN neurons and tremor activity. Significant coherence at the tremor frequency was detected between EMG and neuronal STN activity in 76 out of 145 neurons (52.4%). In contrast, coherence in the beta band occurred only in 10 out of 145 neurons (6.9%). Tremor-coherent STN activity was widely distributed over the STN being more frequent in its dorsal parts (70.8-88.9%) than in its ventral parts (25.0-48.0%). Our results suggest that synchronous neuronal STN activity at the tremor frequency contributes to the pathogenesis of Parkinsonian tremor. The wide-spread spatial distribution of tremor-coherent spike activity argues for the recruitment of an extended network of subthalamic neurons for tremor generation.