B. Schelter

Tremor-correlated cortical activity in essential tremor

BACKGROUND In patients with parkinsonian resting tremor, tremor-correlated activity in the contralateral sensorimotor cortex has been studied by both magnetoencephalography (MEG) and electroencephalography (EEG). In essential tremor, MEG failed to detect cortical involvement. The objective of this study was to investigate whether EEG recording can reveal tremor-correlated cortical activity in patients with essential tremor or enhanced physiological tremor. METHODS Seven patients with essential tremor and three patients with enhanced physiological tremor participated in the study. Unilateral postural tremor was activated by wrist extension on the right or on the left side. Electromyography (EMG) signals arising from the wrist extensor and flexor muscles, and a high-resolution EEG were recorded simultaneously. Coherences between the time series of the rectified tremor EMG and the EEG were estimated. FINDINGS In five of nine arms with essential tremor, we found highly significant coherences at the tremor frequency between the tremor EMG and the EEG. Isocoherence maps illustrating the topography of significant coherences over the scalp showed that the maximum coherences were located over the contralateral sensorimotor cortex. In the patients with enhanced physiological tremor, we were unable to detect consistent significant corticomuscular coherences at the tremor frequency. INTERPRETATION Using simultaneous EEG-EMG recordings, we showed that significant corticomuscular coherences at the tremor frequency can be found in essential tremor. This finding contrasts with a recent study based on MEG recordings. The results suggest that the sensorimotor cortex is involved in the generation of essential tremor, in a similar way to that previously shown in parkinsonian resting tremor.

The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients.

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.

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.

EPILAB: A software package for studies on the prediction of epileptic seizures

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.

A longitudinal study of tremor frequencies in Parkinson’s disease and essential tremor

OBJECTIVE There is evidence that the tremor frequency in essential tremor (ET) decreases with time. Longitudinal studies on the evolution of tremor frequencies in Parkinsons disease (PD) have so far not been published. Here, we present a longitudinal analysis of tremor frequencies in PD and ET. METHODS We analyzed the standardized accelerometric and electromyographic tremor recordings of 53 patients with PD and 38 patients with ET who underwent repeated routine tremor recordings between 1991 and 2002. RESULTS In an average follow-up period of 44.9 months in PD and 50.6 months in ET, the average number of tremor recordings was 3.3 in PD and 3.7 in ET. In both disorders, tremor frequencies tended to decrease with time. The average annual decrease of the tremor frequency was 0.09 Hz/year in Parkinsonian rest tremor, 0.08 Hz/year in Parkinsonian postural tremor and 0.12 Hz/year in ET. CONCLUSIONS The tremor frequency decreases with time in both PD and ET. The similarity of this decrease in PD and ET may point to a common underlying pathophysiological mechanism. SIGNIFICANCE Decreasing tremor frequencies with time may be functionally important by inducing larger tremor amplitudes due to the low-pass filtering properties of muscles and limbs.

Epileptic seizure predictors based on computational intelligence techniques

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.

The impact of latent confounders in directed network analysis in neuroscience

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.

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.

Potential of Low Dose Leuco-Methylthioninium Bis(Hydromethanesulphonate) (LMTM) Monotherapy for Treatment of Mild Alzheimer’s Disease: Cohort Analysis as Modified Primary Outcome in a Phase III Clinical Trial

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 < 0.025 in both comparisons for change in ADAS-cog, ADCS-ADL, MRI atrophy, and glucose uptake. The brain atrophy rate was initially typical of mild AD in both add-on and monotherapy groups, but after 9 months of treatment, the rate in monotherapy patients declined significantly to that reported for normal elderly controls. Differences in severity or diagnosis at baseline between monotherapy and add-on patients did not account for significant differences in favor of monotherapy. Conclusions: The results are consistent with earlier studies in supporting the hypothesis that LMTM might be effective as monotherapy and that 4 mg twice a day may serve as well as higher doses. A further suitably randomized trial is required to test this hypothesis.

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.