Discrimination of Parkinsonian tremor from essential tremor by implementation of a wavelet-based soft-decision technique on EMG and accelerometer signals

A wavelet-decomposition with soft-decision algorithm is used to estimate an approximate power-spectral density (PSD) of both accelerometer and surface EMG signals for the purpose of discrimination of Parkinson tremor from essential tremor. A soft-decision wavelet-based PSD estimation is used with 256 bands for a signal sampled at 800 Hz. The sum of the entropy of the PSD in band 6 (7.8125–9.375 Hz) and band 11 (15.625–17.1875 Hz) is used as a classification factor. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the classification factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. A “voting” between three results obtained from accelerometer signal and two EMG signals is applied to obtain the final discrimination. A total accuracy of discrimination of 85% is obtained

A neural network approach to distinguish Parkinsonian tremor from advanced essential tremor

A new technique for discrimination between Parkinsonian tremor and essential tremor is investigated in this paper. The method is based on spectral analysis of both accelerometer and surface EMG signals with neural networks. The discrimination system consists of two parts: feature extraction part and classification (distinguishing) part. The feature extraction part uses the method of approximate spectral density estimation of the data by implementing the wavelet-based soft decision technique. In the classification part, a machine learning approach is implemented using back-propagation supervised neural network. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the important features used for distinguishing between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance

Neuroimaging and electrophysiology meet invasive neurostimulation for causal interrogations and modulations of brain states

Deep brain stimulation (DBS) has developed over the last twenty years into a highly effective evidenced-based treatment option for neuropsychiatric disorders. Moreover, it has become a fascinating tool to provide illustrative insights into the functioning of brain networks. New anatomical and pathophysiological models of DBS action have accelerated our understanding of neurological and psychiatric disorders and brain functioning. The description of the brain networks arose through the unique ability to illustrate long-range interactions between interconnected brain regions as derived from state-of-the-art neuroimaging (structural, diffusion, and functional MRI) and the opportunity to record local and large-scale brain activity at millisecond temporal resolution (microelectrode recordings, local field potential, electroencephalography, and magnetoencephalography). In the first part of this review, we describe how neuroimaging techniques have led to current understanding of DBS effects, by identifying and refining the DBS targets and illustrate the actual view on the relationships between electrode locations and clinical effects. One step further, we discuss how neuroimaging has shifted the view of localized DBS effects to a modulation of specific brain circuits, which has been possible from the combination of electrode location reconstructions with recently introduced network imaging methods. We highlight how these findings relate to clinical effects, thus postulating neuroimaging as a key factor to understand the mechanisms of DBS action on behavior and clinical effects. In the second part, we show how invasive electrophysiology techniques have been efficiently integrated into the DBS set-up to precisely localize the neuroanatomical targets of DBS based on distinct region-specific patterns of neural activity. Next, we show how multi-site electrophysiological recordings have granted a real-time window into the aberrant brain circuits within and beyond DBS targets to quantify and map the dynamic properties of rhythmic oscillations. We also discuss how DBS alters the transient synchrony states of oscillatory networks in temporal and spatial domains during resting, task-based and motion conditions, and how this modulation of brain states ultimately shapes the functional response. Finally, we show how a successful decoding and management of electrophysiological proxies (beta bursts, phase-amplitude coupling) of aberrant brain circuits was translated into adaptive DBS stimulation paradigms for a targeted and state-dependent invasive electrical neuromodulation

Effects of ON and OFF subthalamic nucleus deep brain stimulation on cortical activation during finger movements tasks: a simultaneous fNIRS and EEG study [Abstract]

Subthalamic nucleus deep brain stimulation (STN-DBS) therapy is an effective treatment for the motor symptoms of advanced Parkinson’s disease (PD). However, the underlying neurophysiological mechanisms for the motor improvement are uncertain. We utilised a simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) neuroimaging approach to map cortical activation changes to motor performance in a PD patient “ON” and “OFF” STN-DBS. Methods The subject was a male (76y) with bilateral STN-DBS (unipolar stimulation at 160Hz and 3.3V). The experimental design consisted of an “OFF” followed by an “ON” stimulation condition. In both conditions, the subject performed a self-paced finger tapping (FT) task followed by a finger sequence (FS) task with his right hand in blocked design (30-s task, 30-s rest, repeated 5 times). During performance of the FT/FS task with the right hand, changes from rest in oxygenated (O2Hb) and deoxygenated haemoglobin concentrations were measured by an fNIRS system (Oxymon MkIII, AMS) from 15 channels covering the contralateral cortical sensorimotor network. EEG signals from 256 channels (GES-300MR, EGI) were collected synchronously with fNIRS signals. Results/Discussion Concomitant with the improved FT/FS task performance, fNIRS results showed a reduction in contralateral cortical sensorimotor network activation (i.e. smaller and less variable increase in O2Hb over the 5 FT/FS task blocks) in the “ON” than “OFF” condition. The EEG results indicated that the mean power in the Beta and Gamma bands were lower in the “ON” than “OFF” condition. However, the mean power in the Delta band, which was approximately at the FT/FS movement frequency (1-3 Hz), was higher in the “ON” than “OFF” condition. Conclusion This case study showed that STN-DBS facilitates voluntary finger movement performance by a more efficient cortical activation pattern to perform the finger movement tasks, possibly by facilitating the voluntary frequency band (Delta) and suppressing the involuntary frequency bands (Beta/Gamma)

Functional connectivity analysis using whole brain and regional network metrics in MS patients

In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. In addition, at the regional anatomical level, the network parameters clustering coefficient and local efficiency were increased in the insula, the superior parietal gyrus and the temporal pole. Our study indicates that modularity as derived from fMRI can be seen as a characteristic connectivity feature that is increased in MS patients compared to HC. Furthermore, specific anatomical regions linked to perception, motor function and cognition were mainly involved in the enhanced local information processing

Pathophysiologie des Tremors

Tremor ist klinisch als rhythmische, oszillierende Bewegung von Körperpartien definiert, die funktionell zu einer Beeinträchtigung der Koordination und Ausführung zielgerichteter Bewegungen führen kann. Er kann Symptom einer Grunderkrankung sein, wie beispielweise der Ruhetremor bei Morbus Parkinson, oder als eigenständige Krankheit auftreten, wie z. B. der essenzielle oder der orthostatische Tremor. Bei der Entstehung von Tremor spielen sowohl zerebrale als auch spinale und muskuläre Mechanismen eine wichtige Rolle. Die vorliegende Arbeit stellt die Ergebnisse neuer bildgebender und elektrophysiologischer Untersuchungen dar, die zu wichtigen Fortschritten in unserem Verständnis der Pathophysiologie von Tremorerkrankungen geführt haben. Wir diskutieren Modelle für die Entstehung des Ruhetremors bei M. Parkinson, des essenziellen und des orthostatischen Tremors. Dabei schildern wir die aktuellen Weiterentwicklungen vom klassischen Generator-Modell mit einer Beteiligung einzelner zerebraler Regionen hin zu einer Netzwerkperspektive, in der pathologische Oszillationen durch Interaktionen in den neuronalen Netzwerken entstehen und sich ausbreiten. Dabei werden insbesondere neue translationale Ansätze vorgestellt, die als Grundlage für die Entwicklung neuer Therapiestrategien dienen könnten

Functional and effective connectivity during focal epileptic seizures [Poster]

The aim of this study was to investigate the dynamics of neuronal networks during focal seizures using dynamic imaging of coherent sources (DICS) (Gross et al. 2001) and renormalized partial directed coherence (RPDC) (Schelter et al. 2009). Ictal EEG recordings from a patient with drug resistant focal epilepsy, due to a focal cortical dysplasia (FCD) in the left parieto-occipital region, (shown by a high resolution 3-T MRI) were analyzed. DICS revealed the neuronal networks concomitant with the location of the FCD, shown by a high resolution 3-T MRI and areas of decreased metabolism shown by functional neuroimaging methods. The sources identified during the seizure onset and propagation phases were similar. Only the causality was different, showing that the strongest source, located in the occipito-temporal region, is most probably a pacemaker/seizure onset zone of the ictal neuronal networks in this case. The DICS analyses of pre-seizure phase showed the sources in the DMN areas of the brain. We can conclude that analyses of multiple habitual seizures of the same patients by the methods of DICS and RPDC gives us valuable information regarding the seizure onset zone and ictal networks. It can be a useful additive tool during the pre-epilepsy surgical investigations of the patients with drug resistant focal epilepsies

Coherent source and connectivity analysis on simultaneously measured EEG and MEG data during isometric contraction

The most well-known non-invasive electric and magnetic field measurement modalities are the electroencephalography (EEG) and magnetoencephalography (MEG). The first aim of the study was to implement the recently developed realistic head model which uses an integrative approach for both the modalities. The second aim of this study was to find the network of coherent sources and the modes of interactions within this network during isometric contraction (ISC) at (15-30 Hz) in healthy subjects. The third aim was to test the effective connectivity revealed by both the modalities analyzing them separately and combined. The Welch periodogram method was used to estimate the coherence spectrum between the EEG and the electromyography (EMG) signals followed by the realistic head modelling and source analysis method dynamic imaging of coherent sources (DICS) to find the network of coherent sources at the individual peak frequency within the beta band in healthy subjects. The last step was to identify the effective connectivity between the identified sources using the renormalized partial directed coherence method. The cortical and sub-cortical network comprised of the primary sensory motor cortex (PSMC), secondary motor area (SMA), and the cerebellum (C). The cortical and sub-cortical network responsible for the isometric contraction was similar in both the modalities when analysing them separately and combined. The SNR was not significantly different between the two modalities separately and combined. However, the coherence values were significantly higher in the combined modality in comparison to each of the modality separately. The effective connectivity analysis revealed plausible additional connections in the combined modality analysis

EEG-EMG-coherence in SDB patients with utilization of a support vector machine-algorithm [Poster Abstract]

Background We investigated whether the EEG-EMG-coherence allows a differentiation between patients with sleep-disordered breathing (SDB) without OSA and SDB-patients with mild, moderate or severe OSA. Methods Polysomnographic recordings of 102 patients with SDB (33 female; age: 53,± 12,4 years) were analyzed with the multitaper coherence method (MTM). Recordings contained 2 EEG-channels (C3 and C4) and a chin EMG-channel for one night. Four epochs (each 30 seconds, classified manually by AASM 2007 criteria) of each sleep stage were marked (1632 epochs in total), which were included in the classification analysis. The collected data sets were supplied to the support vector machine (SVM) algorithm to classify OSA severity. Twenty patients had a mild (RDI ≥10/h and < 15/h), 30 patients had a moderate (RDI ≥15/h and < 30/h) and 27 patients had a severe OSA (RDI ≥30/h). 25 patients had a RDI < 10/h. The AUC (area under the curve) value was calculated for each receiver operator curve (ROC) curve. Results EEG-EMG coherence was able to distinguish between the SDB-patients without OSA and SDB-patients with OSA in each of the 3 severity groups using an SVM algorithm. In mild OSA, the AUC was 0.616 (p = 0.024), in moderate OSA the AUC was 0.659 (p = 0.003), and in severe OSA the AUC was 0.823 (p < 0.001). Conclusions SDB patients with OSA can be differentiated from SDB patients without OSA on the basis of EEG-EMG coherence by using the Multitaper Coherence Method (MTM) and SVM algorithm

Sleep stage classification using spectral analyses and support vector machine algorithm on C3- and C4-EEG signals [Abstract]

Introduction Sleep stage classification currently relies largely on visual classification methods. We tested a new pipeline for automated offline classification based upon power spectrum at six different frequency bands. The pipeline allowed sleep stage classification and provided whole-night visualization of sleep stages. Materials and methods 102 subjects (69 male; 53.74 ± 12.4 years) underwent full-night polysomnography. The recording system included C3- and C4-EEG channels. All signals were measured at sampling rate of 200 Hz. Four epochs (30 seconds each) of each sleep stage (N1, N2, N3, REM, awake) were marked in the visually scored recordings of each one of the 102 patients. Scoring of sleep stages was performed according to AASM 2007-criteria. In total 408 epochs for each sleep stage were included in the sleep stage classification analyses. Recordings of all these epochs were fed into the pipeline to estimate the power spectrum at six different frequency bands, namely from very low frequency (VLF, 0.1-1 Hz) to gamma frequency (30-50 Hz). The power spectrum was measured with a method called multitaper method. In this method the spectrum is estimated by multiplying the data with K windows (i.e tapers).The estimated parameters were given as input to the support vector machine (SVM) algorithm to classify the five different sleep stages based on the mean power amplitude estimated from six different frequency bands. The SVM algorithm was trained with 51 subjects and the testing was done with the other 51 subjects. In order to avoid bias of the training dataset, a 10-fold cross validation was additionally done to check the performance of the SVM algorithm Results The estimated testing accuracy of prediction of the sleep stages was 84.1% for stage N1 using the mean power amplitude from the delta frequency band. Accuracy was 67.8% for stage N2 from the delta frequency band and 74.9% for stage N3 from the VLF. Accuracy was 79.7% for REM stage from the delta frequency band and 84,8% for the wake stage from the theta frequency band. Conclusions We were able to successfully classify the sleep stages using the mean power amplitude at six different frequency bands separately and achieved up to 85% accuracy using the electrophysiological EEG signals. The delta and theta frequency bands gave the best accuracy of classification among all sleep stages